From 3ace7b62a208d5e201455c25ccc1c615579f71ca Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 19 Feb 2026 13:36:43 +0100
Subject: [PATCH 01/24] try nf-metro

---
 .../nf-cmgg-preprocessing_logo_dark.png       | Bin 78416 -> 79933 bytes
 .../nf-cmgg-preprocessing_logo_dark.svg       |  16 +-
 docs/workflow.mmd                             | 197 +++++++++++++
 docs/workflow.svg                             | 271 ++++++++++++++++++
 4 files changed, 476 insertions(+), 8 deletions(-)
 create mode 100644 docs/workflow.mmd
 create mode 100644 docs/workflow.svg

diff --git a/docs/images/nf-cmgg-preprocessing_logo_dark.png b/docs/images/nf-cmgg-preprocessing_logo_dark.png
index 2f4bd13f419811091ec85f16b0267bfe21c79f3c..2f2054b49e9f08c2b5d76141ceca5b742de102bc 100644
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diff --git a/docs/workflow.mmd b/docs/workflow.mmd
new file mode 100644
index 00000000..6e061b84
--- /dev/null
+++ b/docs/workflow.mmd
@@ -0,0 +1,197 @@
+%%metro title: nf-cmgg/preprocessing
+%%metro logo: images/nf-cmgg-preprocessing_logo_dark.png
+%%metro style: light
+
+graph LR
+%%metro entry: bcl_in | BCL
+%%metro entry: fastq_in | FASTQ
+%%metro file: fastq_in | FASTQ
+%%metro file: bcl_in | BCL
+
+%%metro line: main | Main | #4CAF50
+
+%%metro line: demux | Demultiplexing | #4CAF50
+
+%%metro line: align_bwamem1_bamsormadup_samtools_convert | Aligner: BWA MEM, Markdup: Biobambam Bamsormadup | #2196F3
+%%metro line: align_bwamem2_bamsormadup_samtools_convert | Aligner: BWA MEM2, Markdup: Biobambam Bamsormadup | #2196F3
+%%metro line: align_snapaligner_bamsormadup_samtools_convert | Aligner: Snap-aligner, Markdup: Biobambam Bamsormadup | #2196F3
+%%metro line: align_star_bamsormadup_samtools_convert | Aligner: STAR, Markdup: Biobambam Bamsormadup | #2196F3
+%%metro line: align_bowtie2_bamsormadup_samtools_convert | Aligner: Bowtie2, Markdup: Biobambam Bamsormadup | #2196F3
+%%metro line: align_dragen_bamsormadup_samtools_convert | Aligner: Dragen, Markdup: Biobambam Bamsormadup | #2196F3
+%%metro line: align_strobealigner_bamsormadup_samtools_convert | Aligner: Strobe-aligner, Markdup: Biobambam Bamsormadup | #2196F3
+
+%%metro line: align_bwamem1_samtools_sormadup | Aligner: BWA MEM, Markdup: SAMtools Sormadup | #2196F3
+%%metro line: align_bwamem2_samtools_sormadup | Aligner: BWA MEM2, Markdup: SAMtools Sormadup | #2196F3
+%%metro line: align_snapaligner_samtools_sormadup | Aligner: Snap-aligner, Markdup: SAMtools Sormadup | #2196F3
+%%metro line: align_star_samtools_sormadup | Aligner: STAR, Markdup: SAMtools Sormadup | #2196F3
+%%metro line: align_bowtie2_samtools_sormadup | Aligner: Bowtie2, Markdup: SAMtools Sormadup | #2196F3
+%%metro line: align_dragen_samtools_sormadup | Aligner: Dragen, Markdup: SAMtools Sormadup | #2196F3
+%%metro line: align_strobealigner_samtools_sormadup | Aligner: Strobe-aligner, Markdup: SAMtools Sormadup | #2196F3
+
+%%metro line: align_bwamem1_samtools_sort | Aligner: BWA MEM, Markdup: SAMtools Sort | #2196F3
+%%metro line: align_bwamem2_samtools_sort | Aligner: BWA MEM2, Markdup: SAMtools Sort | #2196F3
+%%metro line: align_snapaligner_samtools_sort | Aligner: Snap-aligner, Markdup: SAMtools Sort | #2196F3
+%%metro line: align_star_samtools_sort | Aligner: STAR, Markdup: SAMtools Sort | #2196F3
+%%metro line: align_bowtie2_samtools_sort | Aligner: Bowtie2, Markdup: SAMtools Sort | #2196F3
+%%metro line: align_dragen_samtools_sort | Aligner: Dragen, Markdup: SAMtools Sort | #2196F3
+%%metro line: align_strobealigner_samtools_sort | Aligner: Strobe-aligner, Markdup: SAMtools Sort | #2196F3
+
+%%metro line: coverage | Coverage QC | #f38721ff
+
+%%metro line: bam_qc_roi | BAM QC - ROI| #f32121ff
+%%metro line: bam_qc_wgs | BAM QC - WGS| #f321e9ff
+
+    subgraph demultiplexing [Basecalling & Demultiplexing]
+        bcl_in[BCL]
+        bclconvert[bcl-convert]
+        multiqc_sav[MultiQC-SAV]
+        demultiplex_out[Fastq]
+        sav_out[SAV]
+
+        bcl_in -->|demux| bclconvert
+        bclconvert -->|demux| demultiplex_out
+        bclconvert -->|demux| multiqc_sav
+        multiqc_sav -->|demux| sav_out
+    end
+
+    subgraph genome_alignment [Genome Alignment]
+        alignment_fastq_in[]
+        bwamem[bwa mem]
+        bwamem2[bwa mem2]
+        snapaligner[snap-aligner]
+        star[star]
+        bowtie2[bowtie2]
+        dragen[dragen]
+        strobealigner[strobe-align]
+
+        bamsormadup[biobambam bamsormadup]
+        samtools_convert[samtools convert]
+        samtools_sormadup[samtools sormadup]
+        samtools_sort[samtools sort]
+
+        cram_out[cram]
+
+        alignment_fastq_in -->|align_bwamem1_bamsormadup_samtools_convert,align_bwamem1_samtools_sormadup,align_bwamem1_samtools_sort| bwamem
+        alignment_fastq_in -->|align_bwamem2_bamsormadup_samtools_convert,align_bwamem2_samtools_sormadup,align_bwamem2_samtools_sort| bwamem2
+        alignment_fastq_in -->|align_snapaligner_bamsormadup_samtools_convert,align_snapaligner_samtools_sormadup,align_snapaligner_samtools_sort| snapaligner
+        alignment_fastq_in -->|align_star_bamsormadup_samtools_convert,align_star_samtools_sormadup,align_star_samtools_sort| star
+        alignment_fastq_in -->|align_bowtie2_bamsormadup_samtools_convert,align_bowtie2_samtools_sormadup,align_bowtie2_samtools_sort| bowtie2
+        alignment_fastq_in -->|align_dragen_bamsormadup_samtools_convert,align_dragen_samtools_sormadup,align_dragen_samtools_sort| dragen
+        alignment_fastq_in -->|align_strobealigner_bamsormadup_samtools_convert,align_strobealigner_samtools_sormadup,align_strobealigner_samtools_sort| strobealigner
+
+        bwamem -->|align_bwamem1_bamsormadup_samtools_convert| bamsormadup
+        bwamem2 -->|align_bwamem2_bamsormadup_samtools_convert| bamsormadup
+        snapaligner -->|align_snapaligner_bamsormadup_samtools_convert| bamsormadup
+        star -->|align_star_bamsormadup_samtools_convert| bamsormadup
+        bowtie2 -->|align_bowtie2_bamsormadup_samtools_convert| bamsormadup
+        dragen -->|align_dragen_bamsormadup_samtools_convert| bamsormadup
+        strobealigner -->|align_strobealigner_bamsormadup_samtools_convert| bamsormadup
+
+        bamsormadup -->|align_bwamem1_bamsormadup_samtools_convert| samtools_convert
+        bamsormadup -->|align_bwamem2_bamsormadup_samtools_convert| samtools_convert
+        bamsormadup -->|align_snapaligner_bamsormadup_samtools_convert| samtools_convert
+        bamsormadup -->|align_star_bamsormadup_samtools_convert| samtools_convert
+        bamsormadup -->|align_bowtie2_bamsormadup_samtools_convert| samtools_convert
+        bamsormadup -->|align_dragen_bamsormadup_samtools_convert| samtools_convert
+        bamsormadup -->|align_strobealigner_bamsormadup_samtools_convert| samtools_convert
+
+        bwamem -->|align_bwamem1_samtools_sormadup| samtools_sormadup
+        bwamem2 -->|align_bwamem2_samtools_sormadup| samtools_sormadup
+        snapaligner -->|align_snapaligner_samtools_sormadup| samtools_sormadup
+        star -->|align_star_samtools_sormadup| samtools_sormadup
+        bowtie2 -->|align_bowtie2_samtools_sormadup| samtools_sormadup
+        dragen -->|align_dragen_samtools_sormadup| samtools_sormadup
+        strobealigner -->|align_strobealigner_samtools_sormadup| samtools_sormadup
+
+        bwamem -->|align_bwamem1_samtools_sort| samtools_sort
+        bwamem2 -->|align_bwamem2_samtools_sort| samtools_sort
+        snapaligner -->|align_snapaligner_samtools_sort| samtools_sort
+        star -->|align_star_samtools_sort| samtools_sort
+        bowtie2 -->|align_bowtie2_samtools_sort| samtools_sort
+        dragen -->|align_dragen_samtools_sort| samtools_sort
+        strobealigner -->|align_strobealigner_samtools_sort| samtools_sort
+
+        samtools_convert -->|align_bwamem1_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_bwamem1_samtools_sormadup| cram_out
+        samtools_sort -->|align_bwamem1_samtools_sort| cram_out
+
+        samtools_convert -->|align_bwamem2_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_bwamem2_samtools_sormadup| cram_out
+        samtools_sort -->|align_bwamem2_samtools_sort| cram_out
+
+        samtools_convert -->|align_snapaligner_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_snapaligner_samtools_sormadup| cram_out
+        samtools_sort -->|align_snapaligner_samtools_sort| cram_out
+
+        samtools_convert -->|align_star_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_star_samtools_sormadup| cram_out
+        samtools_sort -->|align_star_samtools_sort| cram_out
+
+        samtools_convert -->|align_bowtie2_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_bowtie2_samtools_sormadup| cram_out
+        samtools_sort -->|align_bowtie2_samtools_sort| cram_out
+
+        samtools_convert -->|align_dragen_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_dragen_samtools_sormadup| cram_out
+        samtools_sort -->|align_dragen_samtools_sort| cram_out
+
+        samtools_convert -->|align_strobealigner_bamsormadup_samtools_convert| cram_out
+        samtools_sormadup -->|align_strobealigner_samtools_sormadup| cram_out
+        samtools_sort -->|align_strobealigner_samtools_sort| cram_out
+
+    end
+
+    subgraph coverage_qc [Coverage QC]
+        coverage_qc_in[]
+        mosdepth[mosdepth]
+        samtools_coverage[samtools coverage]
+        panelcoverage[Panel Coverage]
+        coverage_qc_reports_out[Coverage Reports]
+
+        coverage_qc_in -->|coverage| mosdepth
+        coverage_qc_in -->|coverage| samtools_coverage
+        coverage_qc_in -->|coverage| panelcoverage
+
+        mosdepth -->|coverage| coverage_qc_reports_out
+        samtools_coverage -->|coverage| coverage_qc_reports_out
+        panelcoverage -->|coverage| coverage_qc_reports_out
+
+    end
+
+    subgraph bam_qc [BAM QC]
+        bam_qc_in[]
+        samtools_stats[samtools stats]
+        samtools_flagstat[samtools flagstat]
+        samtools_idxstats[samtools idxstats]
+        picard_multiplemetrics[picard CollectMultipleMetrics]
+        picard_hsmetrics[picard CollectHsMetrics]
+        picard_wgsmetrics[picard CollectWgsMetrics]
+        bam_qc_reports_out[QC Reports]
+
+        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| samtools_stats
+        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| samtools_flagstat
+        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| samtools_idxstats
+        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| picard_multiplemetrics
+        bam_qc_in -->|bam_qc_roi| picard_hsmetrics
+        bam_qc_in -->|bam_qc_wgs| picard_wgsmetrics
+
+        samtools_stats -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
+        samtools_flagstat -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
+        samtools_idxstats -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
+        picard_multiplemetrics -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
+        picard_hsmetrics -->|bam_qc_roi| bam_qc_reports_out
+        picard_wgsmetrics -->|bam_qc_wgs| bam_qc_reports_out
+    end
+
+    md5sum[md5sum]
+    multiqc_main[Main MultiQC]
+    multiqc_library[Library MultiQC]
+
+    fastq_in -->|main| alignment_fastq_in
+    demultiplex_out -->|main| alignment_fastq_in
+    cram_out -->|main| coverage_qc_in
+    cram_out -->|main| bam_qc_in
+    cram_out -->|main| md5sum
+    sav_out -->|main| multiqc_main
+    coverage_qc_reports_out -->|main| multiqc_library
+    bam_qc_reports_out -->|main| multiqc_library
diff --git a/docs/workflow.svg b/docs/workflow.svg
new file mode 100644
index 00000000..d1416d01
--- /dev/null
+++ b/docs/workflow.svg
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+<text x="39.0" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">1</text>
+<text x="53.0" y="72.0" font-size="10.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Basecalling &amp; Demultiplexing</text>
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+<text x="431.5" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">2</text>
+<text x="445.5" y="72.0" font-size="10.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Genome Alignment</text>
+<rect x="1039.0" y="85.0" width="325.0" height="150.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
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+<text x="1133.0225621414913" y="1367.0" font-size="11.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Aligner: Strobe-aligner, Markdup: SAMtools Sort</text>
+<path d="M1097.0225621414913,1391.0 L1121.0225621414913,1391.0" stroke="#f38721ff" stroke-width="3.0" stroke-linecap="round" />
+<text x="1133.0225621414913" y="1391.0" font-size="11.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Coverage QC</text>
+<path d="M1097.0225621414913,1415.0 L1121.0225621414913,1415.0" stroke="#f32121ff" stroke-width="3.0" stroke-linecap="round" />
+<text x="1133.0225621414913" y="1415.0" font-size="11.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">BAM QC - ROI</text>
+<path d="M1097.0225621414913,1439.0 L1121.0225621414913,1439.0" stroke="#f321e9ff" stroke-width="3.0" stroke-linecap="round" />
+<text x="1133.0225621414913" y="1439.0" font-size="11.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">BAM QC - WGS</text>
+<text x="1525.0" y="1575" font-size="8" fill="rgba(150, 150, 150, 0.6)" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" text-anchor="end">created with nf-metro v0.3.0</text>
+</svg>

From 42a5e9413d259c644b9b57e5a8691e37b2e6084a Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 19 Feb 2026 13:38:01 +0100
Subject: [PATCH 02/24] rename files

---
 docs/{workflow.mmd => workflow-dark.mmd} | 0
 docs/{workflow.svg => workflow-dark.svg} | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename docs/{workflow.mmd => workflow-dark.mmd} (100%)
 rename docs/{workflow.svg => workflow-dark.svg} (100%)

diff --git a/docs/workflow.mmd b/docs/workflow-dark.mmd
similarity index 100%
rename from docs/workflow.mmd
rename to docs/workflow-dark.mmd
diff --git a/docs/workflow.svg b/docs/workflow-dark.svg
similarity index 100%
rename from docs/workflow.svg
rename to docs/workflow-dark.svg

From c696749712d296d726479d17cc7c3f848cf7d346 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 20 Feb 2026 11:38:18 +0100
Subject: [PATCH 03/24] more tinkering

---
 docs/workflow-dark.mmd | 296 ++++++++++-------------
 docs/workflow-dark.svg | 521 ++++++++++++++++++++---------------------
 2 files changed, 378 insertions(+), 439 deletions(-)

diff --git a/docs/workflow-dark.mmd b/docs/workflow-dark.mmd
index 6e061b84..5005df0b 100644
--- a/docs/workflow-dark.mmd
+++ b/docs/workflow-dark.mmd
@@ -1,197 +1,143 @@
 %%metro title: nf-cmgg/preprocessing
 %%metro logo: images/nf-cmgg-preprocessing_logo_dark.png
-%%metro style: light
+%%metro style: dark
+
+%%metro line: main | Main | #4CAF50
+%%metro line: align_rna | RNA Alignment | #FF9800
+%%metro line: align_dna | DNA Alignment | #ff1e00ff
+%%metro line: align_postprocessing | Alignment Postprocessing | #9C27B0
+%%metro line: qc | Quality Control | #2196F3
 
-graph LR
-%%metro entry: bcl_in | BCL
-%%metro entry: fastq_in | FASTQ
 %%metro file: fastq_in | FASTQ
 %%metro file: bcl_in | BCL
+%%metro file: cram_out | CRAM
+%%metro file: fastq_out | FASTQ
+%%metro file: reports_out | HTML
 
-%%metro line: main | Main | #4CAF50
-
-%%metro line: demux | Demultiplexing | #4CAF50
-
-%%metro line: align_bwamem1_bamsormadup_samtools_convert | Aligner: BWA MEM, Markdup: Biobambam Bamsormadup | #2196F3
-%%metro line: align_bwamem2_bamsormadup_samtools_convert | Aligner: BWA MEM2, Markdup: Biobambam Bamsormadup | #2196F3
-%%metro line: align_snapaligner_bamsormadup_samtools_convert | Aligner: Snap-aligner, Markdup: Biobambam Bamsormadup | #2196F3
-%%metro line: align_star_bamsormadup_samtools_convert | Aligner: STAR, Markdup: Biobambam Bamsormadup | #2196F3
-%%metro line: align_bowtie2_bamsormadup_samtools_convert | Aligner: Bowtie2, Markdup: Biobambam Bamsormadup | #2196F3
-%%metro line: align_dragen_bamsormadup_samtools_convert | Aligner: Dragen, Markdup: Biobambam Bamsormadup | #2196F3
-%%metro line: align_strobealigner_bamsormadup_samtools_convert | Aligner: Strobe-aligner, Markdup: Biobambam Bamsormadup | #2196F3
-
-%%metro line: align_bwamem1_samtools_sormadup | Aligner: BWA MEM, Markdup: SAMtools Sormadup | #2196F3
-%%metro line: align_bwamem2_samtools_sormadup | Aligner: BWA MEM2, Markdup: SAMtools Sormadup | #2196F3
-%%metro line: align_snapaligner_samtools_sormadup | Aligner: Snap-aligner, Markdup: SAMtools Sormadup | #2196F3
-%%metro line: align_star_samtools_sormadup | Aligner: STAR, Markdup: SAMtools Sormadup | #2196F3
-%%metro line: align_bowtie2_samtools_sormadup | Aligner: Bowtie2, Markdup: SAMtools Sormadup | #2196F3
-%%metro line: align_dragen_samtools_sormadup | Aligner: Dragen, Markdup: SAMtools Sormadup | #2196F3
-%%metro line: align_strobealigner_samtools_sormadup | Aligner: Strobe-aligner, Markdup: SAMtools Sormadup | #2196F3
-
-%%metro line: align_bwamem1_samtools_sort | Aligner: BWA MEM, Markdup: SAMtools Sort | #2196F3
-%%metro line: align_bwamem2_samtools_sort | Aligner: BWA MEM2, Markdup: SAMtools Sort | #2196F3
-%%metro line: align_snapaligner_samtools_sort | Aligner: Snap-aligner, Markdup: SAMtools Sort | #2196F3
-%%metro line: align_star_samtools_sort | Aligner: STAR, Markdup: SAMtools Sort | #2196F3
-%%metro line: align_bowtie2_samtools_sort | Aligner: Bowtie2, Markdup: SAMtools Sort | #2196F3
-%%metro line: align_dragen_samtools_sort | Aligner: Dragen, Markdup: SAMtools Sort | #2196F3
-%%metro line: align_strobealigner_samtools_sort | Aligner: Strobe-aligner, Markdup: SAMtools Sort | #2196F3
-
-%%metro line: coverage | Coverage QC | #f38721ff
-
-%%metro line: bam_qc_roi | BAM QC - ROI| #f32121ff
-%%metro line: bam_qc_wgs | BAM QC - WGS| #f321e9ff
-
-    subgraph demultiplexing [Basecalling & Demultiplexing]
+graph LR
+    %% define nodes
+    bcl_in[]
+    fastq_in[]
+    fastq_out[]
+    cram_out[]
+    reports_out[]
+
+    subgraph demultiplexing
+        %%metro exit: right | main
+        %%metro exit: bottom | qc
         bcl_in[BCL]
         bclconvert[bcl-convert]
-        multiqc_sav[MultiQC-SAV]
-        demultiplex_out[Fastq]
-        sav_out[SAV]
-
-        bcl_in -->|demux| bclconvert
-        bclconvert -->|demux| demultiplex_out
-        bclconvert -->|demux| multiqc_sav
-        multiqc_sav -->|demux| sav_out
+        bcl_in -->|main| bclconvert
     end
 
-    subgraph genome_alignment [Genome Alignment]
-        alignment_fastq_in[]
-        bwamem[bwa mem]
-        bwamem2[bwa mem2]
-        snapaligner[snap-aligner]
-        star[star]
-        bowtie2[bowtie2]
-        dragen[dragen]
-        strobealigner[strobe-align]
-
-        bamsormadup[biobambam bamsormadup]
-        samtools_convert[samtools convert]
-        samtools_sormadup[samtools sormadup]
-        samtools_sort[samtools sort]
+    subgraph fastq_qc
+        fastp[fastp]
+        falco[falco]
+        bclconvert -->|main| fastp
+        bclconvert -->|main| fastq_out
+        bclconvert -->|qc| falco
+        fastq_in -->|main| fastp
+    end
 
-        cram_out[cram]
-
-        alignment_fastq_in -->|align_bwamem1_bamsormadup_samtools_convert,align_bwamem1_samtools_sormadup,align_bwamem1_samtools_sort| bwamem
-        alignment_fastq_in -->|align_bwamem2_bamsormadup_samtools_convert,align_bwamem2_samtools_sormadup,align_bwamem2_samtools_sort| bwamem2
-        alignment_fastq_in -->|align_snapaligner_bamsormadup_samtools_convert,align_snapaligner_samtools_sormadup,align_snapaligner_samtools_sort| snapaligner
-        alignment_fastq_in -->|align_star_bamsormadup_samtools_convert,align_star_samtools_sormadup,align_star_samtools_sort| star
-        alignment_fastq_in -->|align_bowtie2_bamsormadup_samtools_convert,align_bowtie2_samtools_sormadup,align_bowtie2_samtools_sort| bowtie2
-        alignment_fastq_in -->|align_dragen_bamsormadup_samtools_convert,align_dragen_samtools_sormadup,align_dragen_samtools_sort| dragen
-        alignment_fastq_in -->|align_strobealigner_bamsormadup_samtools_convert,align_strobealigner_samtools_sormadup,align_strobealigner_samtools_sort| strobealigner
-
-        bwamem -->|align_bwamem1_bamsormadup_samtools_convert| bamsormadup
-        bwamem2 -->|align_bwamem2_bamsormadup_samtools_convert| bamsormadup
-        snapaligner -->|align_snapaligner_bamsormadup_samtools_convert| bamsormadup
-        star -->|align_star_bamsormadup_samtools_convert| bamsormadup
-        bowtie2 -->|align_bowtie2_bamsormadup_samtools_convert| bamsormadup
-        dragen -->|align_dragen_bamsormadup_samtools_convert| bamsormadup
-        strobealigner -->|align_strobealigner_bamsormadup_samtools_convert| bamsormadup
-
-        bamsormadup -->|align_bwamem1_bamsormadup_samtools_convert| samtools_convert
-        bamsormadup -->|align_bwamem2_bamsormadup_samtools_convert| samtools_convert
-        bamsormadup -->|align_snapaligner_bamsormadup_samtools_convert| samtools_convert
-        bamsormadup -->|align_star_bamsormadup_samtools_convert| samtools_convert
-        bamsormadup -->|align_bowtie2_bamsormadup_samtools_convert| samtools_convert
-        bamsormadup -->|align_dragen_bamsormadup_samtools_convert| samtools_convert
-        bamsormadup -->|align_strobealigner_bamsormadup_samtools_convert| samtools_convert
-
-        bwamem -->|align_bwamem1_samtools_sormadup| samtools_sormadup
-        bwamem2 -->|align_bwamem2_samtools_sormadup| samtools_sormadup
-        snapaligner -->|align_snapaligner_samtools_sormadup| samtools_sormadup
-        star -->|align_star_samtools_sormadup| samtools_sormadup
-        bowtie2 -->|align_bowtie2_samtools_sormadup| samtools_sormadup
-        dragen -->|align_dragen_samtools_sormadup| samtools_sormadup
-        strobealigner -->|align_strobealigner_samtools_sormadup| samtools_sormadup
-
-        bwamem -->|align_bwamem1_samtools_sort| samtools_sort
-        bwamem2 -->|align_bwamem2_samtools_sort| samtools_sort
-        snapaligner -->|align_snapaligner_samtools_sort| samtools_sort
-        star -->|align_star_samtools_sort| samtools_sort
-        bowtie2 -->|align_bowtie2_samtools_sort| samtools_sort
-        dragen -->|align_dragen_samtools_sort| samtools_sort
-        strobealigner -->|align_strobealigner_samtools_sort| samtools_sort
-
-        samtools_convert -->|align_bwamem1_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_bwamem1_samtools_sormadup| cram_out
-        samtools_sort -->|align_bwamem1_samtools_sort| cram_out
-
-        samtools_convert -->|align_bwamem2_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_bwamem2_samtools_sormadup| cram_out
-        samtools_sort -->|align_bwamem2_samtools_sort| cram_out
-
-        samtools_convert -->|align_snapaligner_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_snapaligner_samtools_sormadup| cram_out
-        samtools_sort -->|align_snapaligner_samtools_sort| cram_out
-
-        samtools_convert -->|align_star_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_star_samtools_sormadup| cram_out
-        samtools_sort -->|align_star_samtools_sort| cram_out
-
-        samtools_convert -->|align_bowtie2_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_bowtie2_samtools_sormadup| cram_out
-        samtools_sort -->|align_bowtie2_samtools_sort| cram_out
-
-        samtools_convert -->|align_dragen_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_dragen_samtools_sormadup| cram_out
-        samtools_sort -->|align_dragen_samtools_sort| cram_out
-
-        samtools_convert -->|align_strobealigner_bamsormadup_samtools_convert| cram_out
-        samtools_sormadup -->|align_strobealigner_samtools_sormadup| cram_out
-        samtools_sort -->|align_strobealigner_samtools_sort| cram_out
+    subgraph alignment_dna
+        bwamem[BWA-MEM]
+        bwamem2[BWA-MEM2]
+        dragmap[Dragmap]
+        snapaligner[SNAP]
+        strobealign[StrobeAlign]
+        _dna_bam_out[]
+
+        fastp -->|align_dna| bwamem
+        fastp -->|align_dna| bwamem2
+        fastp -->|align_dna| dragmap
+        fastp -->|align_dna| snapaligner
+        fastp -->|align_dna| strobealign
+    end
 
+    subgraph alignment_rna
+        star[STAR]
+        _rna_bam_out[]
+
+        fastp -->|align_rna| star
     end
 
-    subgraph coverage_qc [Coverage QC]
-        coverage_qc_in[]
-        mosdepth[mosdepth]
-        samtools_coverage[samtools coverage]
-        panelcoverage[Panel Coverage]
-        coverage_qc_reports_out[Coverage Reports]
+    subgraph alignment_postprocessing
+        _postprocess_bam_in[]
+        bamsormadup[bamsormadup]
+        samtools_sort[samtools sort]
+        samtools_sormadup[samtools sormadup]
+        samtools_convert[samtools convert]
+
+        bwamem -->|align_postprocessing| _postprocess_bam_in
+        bwamem2 -->|align_postprocessing| _postprocess_bam_in
+        dragmap -->|align_postprocessing| _postprocess_bam_in
+        snapaligner -->|align_postprocessing| _postprocess_bam_in
+        strobealign -->|align_postprocessing| _postprocess_bam_in
+        star -->|align_postprocessing| _postprocess_bam_in
 
-        coverage_qc_in -->|coverage| mosdepth
-        coverage_qc_in -->|coverage| samtools_coverage
-        coverage_qc_in -->|coverage| panelcoverage
+        _postprocess_bam_in -->|align_postprocessing| bamsormadup
+        _postprocess_bam_in -->|align_postprocessing| samtools_sort
+        _postprocess_bam_in -->|align_postprocessing| samtools_sormadup
+        _postprocess_bam_in -->|align_postprocessing| samtools_convert
 
-        mosdepth -->|coverage| coverage_qc_reports_out
-        samtools_coverage -->|coverage| coverage_qc_reports_out
-        panelcoverage -->|coverage| coverage_qc_reports_out
+        bamsormadup -->|align_postprocessing| samtools_convert
 
+        samtools_convert -->|align_postprocessing| cram_out
+        samtools_sort -->|align_postprocessing| cram_out
+        samtools_sormadup -->|align_postprocessing| cram_out
     end
 
-    subgraph bam_qc [BAM QC]
-        bam_qc_in[]
-        samtools_stats[samtools stats]
+   subgraph alignment_qc
+        %%metro direction: TB
         samtools_flagstat[samtools flagstat]
+        samtools_stats[samtools stats]
         samtools_idxstats[samtools idxstats]
-        picard_multiplemetrics[picard CollectMultipleMetrics]
-        picard_hsmetrics[picard CollectHsMetrics]
-        picard_wgsmetrics[picard CollectWgsMetrics]
-        bam_qc_reports_out[QC Reports]
-
-        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| samtools_stats
-        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| samtools_flagstat
-        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| samtools_idxstats
-        bam_qc_in -->|bam_qc_roi,bam_qc_wgs| picard_multiplemetrics
-        bam_qc_in -->|bam_qc_roi| picard_hsmetrics
-        bam_qc_in -->|bam_qc_wgs| picard_wgsmetrics
-
-        samtools_stats -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
-        samtools_flagstat -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
-        samtools_idxstats -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
-        picard_multiplemetrics -->|bam_qc_roi,bam_qc_wgs| bam_qc_reports_out
-        picard_hsmetrics -->|bam_qc_roi| bam_qc_reports_out
-        picard_wgsmetrics -->|bam_qc_wgs| bam_qc_reports_out
-    end
+        picard_collectmultiplemetrics[picard collectmultiplemetrics]
 
-    md5sum[md5sum]
-    multiqc_main[Main MultiQC]
-    multiqc_library[Library MultiQC]
-
-    fastq_in -->|main| alignment_fastq_in
-    demultiplex_out -->|main| alignment_fastq_in
-    cram_out -->|main| coverage_qc_in
-    cram_out -->|main| bam_qc_in
-    cram_out -->|main| md5sum
-    sav_out -->|main| multiqc_main
-    coverage_qc_reports_out -->|main| multiqc_library
-    bam_qc_reports_out -->|main| multiqc_library
+        cram_out -->|qc| samtools_flagstat
+        cram_out -->|qc| samtools_stats
+        cram_out -->|qc| samtools_idxstats
+        cram_out -->|qc| picard_collectmultiplemetrics
+   end
+
+   subgraph alignment_qc_wgs
+        picard_collectwgsmetrics[picard collectwgsmetrics]
+        cram_out -->|qc| picard_collectwgsmetrics
+   end
+
+   subgraph alignment_qc_targetted
+        picard_collecthsmetrics[picard collecthsmetrics]
+        cram_out -->|qc| picard_collecthsmetrics
+   end
+
+   subgraph coverage_qc
+        mosdepth[mosdepth]
+        samtools_coverage[samtools coverage]
+        panelcoverage[panelcoverage]
+
+        cram_out -->|qc| mosdepth
+        cram_out -->|qc| samtools_coverage
+        cram_out -->|qc| panelcoverage
+   end
+
+    subgraph reporting
+    mqc_sav[MultiQC-SAV]
+    mqc_library[Library MultiQC]
+    reports_out[QC Reports]
+
+    bclconvert -->|qc| mqc_sav
+    fastp -->|qc| mqc_library
+    falco -->|qc| mqc_library
+    samtools_flagstat -->|qc| mqc_library
+    samtools_stats -->|qc| mqc_library
+    samtools_idxstats -->|qc| mqc_library
+    picard_collectmultiplemetrics -->|qc| mqc_library
+    picard_collectwgsmetrics -->|qc| mqc_library
+    picard_collecthsmetrics -->|qc| mqc_library
+    mosdepth -->|qc| mqc_library
+    samtools_coverage -->|qc| mqc_library
+    panelcoverage -->|qc| mqc_library
+
+    mqc_sav -->|qc| reports_out
+    mqc_library -->|qc| reports_out
+    end
diff --git a/docs/workflow-dark.svg b/docs/workflow-dark.svg
index d1416d01..621e5caa 100644
--- a/docs/workflow-dark.svg
+++ b/docs/workflow-dark.svg
@@ -1,271 +1,264 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"
-     width="1555" height="1583" viewBox="0 0 1555 1583">
+     width="1989" height="1052" viewBox="0 0 1989 1052">
 <defs>
 </defs>
-<rect x="0" y="0" width="1555" height="1583" fill="#2b2b2b" />
-<rect x="30.0" y="85.0" width="342.5" height="150.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
+<rect x="0" y="0" width="1989" height="1052" fill="#2b2b2b" />
+<rect x="30.0" y="85.0" width="209.5" height="70.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
 <circle cx="39.0" cy="72.0" r="9" fill="#333333" />
 <text x="39.0" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">1</text>
-<text x="53.0" y="72.0" font-size="10.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Basecalling &amp; Demultiplexing</text>
-<rect x="422.5" y="85.0" width="566.5" height="390.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="431.5" cy="72.0" r="9" fill="#333333" />
-<text x="431.5" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">2</text>
-<text x="445.5" y="72.0" font-size="10.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Genome Alignment</text>
-<rect x="1039.0" y="85.0" width="325.0" height="150.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
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" 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+<path d="M371.5869980879541,908.0 L395.5869980879541,908.0" stroke="#4CAF50" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="908.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Main</text>
+<path d="M371.5869980879541,932.0 L395.5869980879541,932.0" stroke="#FF9800" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="932.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">RNA Alignment</text>
+<path d="M371.5869980879541,956.0 L395.5869980879541,956.0" stroke="#ff1e00ff" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="956.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">DNA Alignment</text>
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+<text x="407.5869980879541" y="980.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Alignment Postprocessing</text>
+<path d="M371.5869980879541,1004.0 L395.5869980879541,1004.0" stroke="#2196F3" stroke-width="3.0" stroke-linecap="round" />
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+<text x="1959.0" y="1044.0" font-size="8" fill="rgba(150, 150, 150, 0.6)" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" text-anchor="end">created with nf-metro v0.4.4</text>
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From 8d6af7f16bb4f7e022f241c50f47ab5887c4ddca Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Mon, 23 Feb 2026 15:23:15 +0100
Subject: [PATCH 04/24] fix bclconvert config

---
 conf/modules.config | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conf/modules.config b/conf/modules.config
index 0c65d04c..f105629f 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -13,7 +13,7 @@
 process {
 
     // BCL convert
-    withName: '.*BCL_DEMULTIPLEX:BCLCONVERT' {
+    withName: '.*BCLCONVERT' {
         cpus     = 16
         memory   = { 64.GB * task.attempt }
         ext.args = {

From 80aab01d418650440383bbf8f1e0995786e604bf Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Wed, 25 Feb 2026 08:52:00 +0100
Subject: [PATCH 05/24] add demux logging

---
 workflows/preprocessing.nf | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/workflows/preprocessing.nf b/workflows/preprocessing.nf
index e402edc9..f701ddc6 100644
--- a/workflows/preprocessing.nf
+++ b/workflows/preprocessing.nf
@@ -77,12 +77,17 @@ workflow PREPROCESSING {
         BCLCONVERT.out.logs,
     )
 
-    generateReadgroup(
-        BCLCONVERT.out.reports.map { meta, reports ->
-            return [meta, reports.find { report -> report.name == "fastq_list.csv" }]
-        },
-        BCLCONVERT.out.fastq,
-    ).map { meta, fastq -> [meta.readgroup.SM, meta, fastq] }.set { ch_demultiplexed_fastq }
+    ch_fastq_with_meta = ch_fastq_with_meta.mix(
+        generateReadgroup(
+            BCLCONVERT.out.reports.map { meta, reports ->
+                return [meta, reports.find { report -> report.name == "fastq_list.csv" }]
+            },
+            BCLCONVERT.out.fastq,
+        )
+    )
+    ch_fastq_with_meta.dump(tag: "DEMULTIPLEX: fastq with meta", pretty: true)
+
+    ch_fastq_with_meta.map { meta, fastq -> [meta.readgroup.SM, meta, fastq] }.set { ch_demultiplexed_fastq }
 
     ch_illumina_flowcell.info
         .flatten()

From d986365dc5f702d8c5b671a7e5c3642c045fe800 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Wed, 25 Feb 2026 08:55:57 +0100
Subject: [PATCH 06/24] typo

---
 workflows/preprocessing.nf | 21 +++++++++------------
 1 file changed, 9 insertions(+), 12 deletions(-)

diff --git a/workflows/preprocessing.nf b/workflows/preprocessing.nf
index f701ddc6..849d7272 100644
--- a/workflows/preprocessing.nf
+++ b/workflows/preprocessing.nf
@@ -37,8 +37,8 @@ include { getGenomeAttribute         } from '../subworkflows/local/utils_nfcore_
 workflow PREPROCESSING {
     take:
     ch_samplesheet // channel: samplesheet read in from --input
-    genomes        // map: genome reference files
-    genelists      // file: directory containing genelist bed files for coverage analysis
+    genomes // map: genome reference files
+    genelists // file: directory containing genelist bed files for coverage analysis
 
     main:
     ch_multiqc_files = channel.empty()
@@ -77,14 +77,12 @@ workflow PREPROCESSING {
         BCLCONVERT.out.logs,
     )
 
-    ch_fastq_with_meta = ch_fastq_with_meta.mix(
-        generateReadgroup(
-            BCLCONVERT.out.reports.map { meta, reports ->
-                return [meta, reports.find { report -> report.name == "fastq_list.csv" }]
-            },
-            BCLCONVERT.out.fastq,
-        )
-    )
+    generateReadgroup(
+        BCLCONVERT.out.reports.map { meta, reports ->
+            return [meta, reports.find { report -> report.name == "fastq_list.csv" }]
+        },
+        BCLCONVERT.out.fastq,
+    ).set { ch_fastq_with_meta }
     ch_fastq_with_meta.dump(tag: "DEMULTIPLEX: fastq with meta", pretty: true)
 
     ch_fastq_with_meta.map { meta, fastq -> [meta.readgroup.SM, meta, fastq] }.set { ch_demultiplexed_fastq }
@@ -335,8 +333,7 @@ workflow PREPROCESSING {
     //
     // Collate and save software versions
     //
-    def topic_versions = channel
-        .topic("versions")
+    def topic_versions = channel.topic("versions")
         .distinct()
         .branch { entry ->
             versions_file: entry instanceof Path

From 857ddeea8b75c844db50ad6cbfae86181e430850 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Wed, 25 Feb 2026 10:10:33 +0100
Subject: [PATCH 07/24] fix rg function

---
 modules/nf-core/bclconvert/main.nf | 35 +++++++++++-------------------
 1 file changed, 13 insertions(+), 22 deletions(-)

diff --git a/modules/nf-core/bclconvert/main.nf b/modules/nf-core/bclconvert/main.nf
index 6f207277..40be3cdf 100644
--- a/modules/nf-core/bclconvert/main.nf
+++ b/modules/nf-core/bclconvert/main.nf
@@ -106,9 +106,9 @@ process BCLCONVERT {
 
 def generateReadgroup(ch_fastq_list_csv, ch_fastq) {
     return ch_fastq_list_csv
-        .collect() // make it a value channel
-        .map { meta, csv_file ->
-            def fastq_metadata = []
+        .join(ch_fastq, by: [0])
+        .map { meta, csv_file, fastq_list ->
+            def meta_fastq = []
             csv_file
                 .splitCsv(header: true)
                 .each { row ->
@@ -123,25 +123,16 @@ def generateReadgroup(ch_fastq_list_csv, ch_fastq) {
                     rg.LB = row.RGLB ? row.RGLB : ""
                     rg.PL = "ILLUMINA"
 
-                    // replace the meta id with the sample name
-                    def new_meta = [id: row.RGSM, readgroup: rg]
-                    // Return the new meta with fastq file
-                    fastq_metadata << [new_meta, file(row.Read1File).name]
-                    if (row.Read2File) {
-                        fastq_metadata << [new_meta, file(row.Read2File).name]
-                    }
+                    // dereference the fastq files in the csv
+                    def fastq1 = fastq_list.find { fq -> file(fq).name == file(row.Read1File).name }
+                    def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
+
+                    // set fastq metadata
+                    def new_meta = meta + [id: row.RGSM, readgroup: rg, single_end: !fastq2]
+
+                    meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
                 }
-            return [meta, fastq_metadata]
-        }
-        .join(ch_fastq, by:[0]) // -> [ meta, [fq_meta, fastq_filename], [fastq_file, ...] ]
-        .transpose(by:[2]) // -> [ meta, [fq_meta, fastq_filename], fastq_file ]
-        .map { meta, fastq_metadata, fastq_file ->
-            def fastq_meta = fastq_metadata.find { _meta, filename -> filename == file(fastq_file).name }
-            return [meta + fastq_meta[0], file(fastq_file)]
-        }
-        .groupTuple(by: [0])
-        .map { meta, fastq ->
-            meta.single_end = fastq.size() == 1
-            return [meta, fastq.flatten()]
+            return meta_fastq
         }
+        .flatMap()
 }

From 773d3d7cc487f17fd721eaa0b61ab798294ea43a Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Wed, 25 Feb 2026 10:11:51 +0100
Subject: [PATCH 08/24] fix rg function

---
 modules.json                               |  5 ++-
 modules/nf-core/bclconvert/bclconvert.diff | 51 ++++++++++++++++++++++
 modules/nf-core/bclconvert/main.nf         |  2 +-
 3 files changed, 55 insertions(+), 3 deletions(-)
 create mode 100644 modules/nf-core/bclconvert/bclconvert.diff

diff --git a/modules.json b/modules.json
index 7a04dbc8..6d2aefc7 100644
--- a/modules.json
+++ b/modules.json
@@ -7,8 +7,9 @@
                 "nf-core": {
                     "bclconvert": {
                         "branch": "master",
-                        "git_sha": "547b6a0078c2f4499b64d59edb8ae038b31971b2",
-                        "installed_by": ["modules"]
+                        "git_sha": "18e1a9566d76569b8a40dc60184d4eb7413cf6e1",
+                        "installed_by": ["modules"],
+                        "patch": "modules/nf-core/bclconvert/bclconvert.diff"
                     },
                     "biobambam/bamsormadup": {
                         "branch": "master",
diff --git a/modules/nf-core/bclconvert/bclconvert.diff b/modules/nf-core/bclconvert/bclconvert.diff
new file mode 100644
index 00000000..e27cb946
--- /dev/null
+++ b/modules/nf-core/bclconvert/bclconvert.diff
@@ -0,0 +1,51 @@
+Changes in component 'nf-core/bclconvert'
+'modules/nf-core/bclconvert/LICENSE' is unchanged
+'modules/nf-core/bclconvert/Dockerfile' is unchanged
+'modules/nf-core/bclconvert/README.md' is unchanged
+'modules/nf-core/bclconvert/.gitignore' is unchanged
+'modules/nf-core/bclconvert/meta.yml' is unchanged
+Changes in 'bclconvert/main.nf':
+--- modules/nf-core/bclconvert/main.nf
++++ modules/nf-core/bclconvert/main.nf
+@@ -103,3 +103,36 @@
+     echo "fake InterOp file" > output/InterOp/TileMetricsOut.bin
+     """
+ }
++
++def generateReadgroup(ch_fastq_list_csv, ch_fastq) {
++    return ch_fastq_list_csv
++        .join(ch_fastq, by: [0])
++        .map { meta, csv_file, fastq_list ->
++            def meta_fastq = []
++            csv_file
++                .splitCsv(header: true)
++                .each { row ->
++                    // Create the readgroup tuple
++                    // RGID,RGSM,RGLB,Lane,Read1File,Read2File
++                    def rg = [:]
++                    // row.RGID is index1.index2.lane
++                    rg.ID = row.RGID
++                    // RGPU is a custom column in the samplesheet containing the flowcell ID
++                    rg.PU = row.RGPU ? row.RGPU : meta.id + "." + row.Lane
++                    rg.SM = row.RGSM
++                    rg.LB = row.RGLB ? row.RGLB : ""
++                    rg.PL = "ILLUMINA"
++
++                    // dereference the fastq files in the csv
++                    def fastq1 = fastq_list.find { fq -> file(fq).name == file(row.Read1File).name }
++                    def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
++
++                    // set fastq metadata
++                    def new_meta = meta + [id: row.RGSM, readgroup: rg, single_end: !fastq2]
++
++                    meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
++                }
++            return meta_fastq
++        }
++        .flatMap()
++}
+
+'modules/nf-core/bclconvert/tests/main.nf.test.snap' is unchanged
+'modules/nf-core/bclconvert/tests/nextflow.config' is unchanged
+'modules/nf-core/bclconvert/tests/main.nf.test' is unchanged
+************************************************************
diff --git a/modules/nf-core/bclconvert/main.nf b/modules/nf-core/bclconvert/main.nf
index 40be3cdf..1e79e24c 100644
--- a/modules/nf-core/bclconvert/main.nf
+++ b/modules/nf-core/bclconvert/main.nf
@@ -1,5 +1,5 @@
 process BCLCONVERT {
-    tag { "${meta.lane}" ? "${meta.id}" + "." + "${meta.lane}" : "${meta.id}" }
+    tag "${ meta.lane ? meta.id + "." + meta.lane : meta.id }"
     label 'process_high'
 
     container "nf-core/bclconvert:4.4.6"

From d85af27f17cf9f9b27044b517fe4401ccddf2201 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Wed, 25 Feb 2026 10:23:44 +0100
Subject: [PATCH 09/24] catch library from sampleinfo if applicable

---
 workflows/preprocessing.nf | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/workflows/preprocessing.nf b/workflows/preprocessing.nf
index 849d7272..225c1ba4 100644
--- a/workflows/preprocessing.nf
+++ b/workflows/preprocessing.nf
@@ -97,7 +97,12 @@ workflow PREPROCESSING {
     ch_demultiplexed_fastq
         .combine(ch_sampleinfo, by: 0)
         .map { _samplename, meta, fastq, sampleinfo ->
-            def new_meta = meta + sampleinfo
+            if (sampleinfo.library) {
+                new_rg = meta.readgroup + ['LB': sampleinfo.library]
+            } else {
+                new_rg = meta.readgroup
+            }
+            def new_meta = meta + sampleinfo + ['readgroup': new_rg]
             return [new_meta, fastq]
         }
         .groupTuple(by: [0])

From 28350ca6bc7530beeeedc087693d38b63bb44495 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Wed, 25 Feb 2026 11:40:09 +0100
Subject: [PATCH 10/24] fix bclconvert meta.id

---
 modules/nf-core/bclconvert/bclconvert.diff | 2 +-
 modules/nf-core/bclconvert/main.nf         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/nf-core/bclconvert/bclconvert.diff b/modules/nf-core/bclconvert/bclconvert.diff
index e27cb946..e850ef91 100644
--- a/modules/nf-core/bclconvert/bclconvert.diff
+++ b/modules/nf-core/bclconvert/bclconvert.diff
@@ -36,7 +36,7 @@ Changes in 'bclconvert/main.nf':
 +                    def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
 +
 +                    // set fastq metadata
-+                    def new_meta = meta + [id: row.RGSM, readgroup: rg, single_end: !fastq2]
++                    def new_meta = meta + [id: fastq1.getSimpleName().toString() - ~/_R[0-9]_001.*$/, readgroup: rg, single_end: !fastq2]
 +
 +                    meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
 +                }
diff --git a/modules/nf-core/bclconvert/main.nf b/modules/nf-core/bclconvert/main.nf
index 1e79e24c..7010ce8a 100644
--- a/modules/nf-core/bclconvert/main.nf
+++ b/modules/nf-core/bclconvert/main.nf
@@ -128,7 +128,7 @@ def generateReadgroup(ch_fastq_list_csv, ch_fastq) {
                     def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
 
                     // set fastq metadata
-                    def new_meta = meta + [id: row.RGSM, readgroup: rg, single_end: !fastq2]
+                    def new_meta = meta + [id: fastq1.getSimpleName().toString() - ~/_R[0-9]_001.*$/, readgroup: rg, single_end: !fastq2]
 
                     meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
                 }

From ba9f85606de928ceaf94be66bf1019c8ac32c36f Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 26 Feb 2026 12:33:01 +0100
Subject: [PATCH 11/24] add cgroup metrics plugin

---
 nextflow.config | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nextflow.config b/nextflow.config
index a5ceb27d..1a8c1156 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -269,6 +269,7 @@ manifest {
 // Nextflow plugins
 plugins {
     id 'nf-schema@2.6.1'
+    id 'nf-cgroup-metrics@1.0.0'
 }
 
 validation {

From 252bf444a9f36a2f1ea87cc1f8221960142bf0cb Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 26 Feb 2026 14:54:05 +0100
Subject: [PATCH 12/24] Remove nf-cgroup-metrics plugin from config

---
 nextflow.config | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nextflow.config b/nextflow.config
index 1a8c1156..a5ceb27d 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -269,7 +269,6 @@ manifest {
 // Nextflow plugins
 plugins {
     id 'nf-schema@2.6.1'
-    id 'nf-cgroup-metrics@1.0.0'
 }
 
 validation {

From ccb4c57662e136374da8f82580919678aa58f251 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 5 Mar 2026 14:24:15 +0100
Subject: [PATCH 13/24] Feat: MultiQC SAV (#160)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* add mqc_sav module

* fix bclconvert config

* add demux logging

* typo

* fix rg function

* fix rg function

* catch library from sampleinfo if applicable

* fix bclconvert meta.id

* add cgroup metrics plugin

* Remove nf-cgroup-metrics plugin from config

* update multiqcsav and multiqc

* mv rg function to lib

* add mqcsav and fix plumbing

* changelog

* fix syntax error

* Replace lib with function in subwf to please linter

* update test config, fix issues

* ditch conda from CI and update docs a little

* fix file staging

* fix linting

* tinkering with configs

* fix nf-test profiles

* more work

* linting

* mqc_sav attempt #1

* Refactor MultiQC summary file handling and improve metadata management

* fix test samplesheet

* bump mqc_cmgg container

* fix mqc output path

* update modules + patch

* fix multi-lane processing

* fix SAV titleé

* fix outputs and patch module

* fix snap config process selector

* set version to 3.0.0

* final fixed for multilane run qc (hopefully)

* fix mqc inputs

* move bclconvert rg function to subwf

* fix final tests

* let's hope we're done now...

* fix main wf errors

* fix tests (again)

* test error

* f*ck it

* f*ck it
---
 .github/ISSUE_TEMPLATE/bug_report.yml         |   2 +-
 .github/actions/nf-test/action.yml            |  10 -
 .github/workflows/clean-up.yml                |   6 +-
 .github/workflows/nf-test.yml                 |   6 +-
 .gitignore                                    |   1 +
 .nf-core.yml                                  |   2 +-
 CHANGELOG.md                                  |   2 +
 README.md                                     |   1 -
 assets/multiqc_config.yml                     |   3 +-
 conf/modules.config                           |  26 +--
 conf/test.config                              |  12 +-
 docs/usage.md                                 |  40 ++--
 main.nf                                       | 162 ++++++-------
 modules.json                                  |  26 ++-
 modules/nf-core/bclconvert/bclconvert.diff    |  51 ----
 modules/nf-core/bclconvert/main.nf            |  37 +--
 modules/nf-core/bclconvert/meta.yml           |   8 +-
 modules/nf-core/bclconvert/tests/main.nf.test |   8 +-
 .../bclconvert/tests/main.nf.test.snap        |  50 +---
 modules/nf-core/bowtie2/align/main.nf         |   2 +
 .../bowtie2/align/tests/main.nf.test.snap     |  28 +--
 modules/nf-core/bwamem2/mem/bwamem2-mem.diff  |   8 +-
 modules/nf-core/bwamem2/mem/main.nf           |  47 ++--
 modules/nf-core/bwamem2/mem/meta.yml          |  19 ++
 .../nf-core/bwamem2/mem/tests/main.nf.test    |   8 +-
 .../bwamem2/mem/tests/main.nf.test.snap       | 109 ++++-----
 modules/nf-core/multiqc/main.nf               |  28 +--
 modules/nf-core/multiqc/meta.yml              | 128 +++++-----
 modules/nf-core/multiqc/multiqc.diff          |  27 ---
 modules/nf-core/multiqc/tests/main.nf.test    | 129 +++++++----
 .../nf-core/multiqc/tests/main.nf.test.snap   | 109 +++++++--
 modules/nf-core/multiqcsav/environment.yml    |  13 ++
 modules/nf-core/multiqcsav/main.nf            |  53 +++++
 modules/nf-core/multiqcsav/meta.yml           | 139 +++++++++++
 modules/nf-core/multiqcsav/tests/main.nf.test | 136 +++++++++++
 .../multiqcsav/tests/main.nf.test.snap        |  96 ++++++++
 .../nf-core/picard/collecthsmetrics/main.nf   |  40 ++--
 .../nf-core/picard/collecthsmetrics/meta.yml  |   4 +-
 .../picard-collecthsmetrics.diff              |  27 +--
 .../collecthsmetrics/tests/main.nf.test       |  25 +-
 .../collecthsmetrics/tests/main.nf.test.snap  |  44 ++--
 .../picard/collectmultiplemetrics/main.nf     |  27 +--
 .../picard/collectmultiplemetrics/meta.yml    |   4 +-
 .../picard-collectmultiplemetrics.diff        |  28 +--
 .../collectmultiplemetrics/tests/main.nf.test | 144 +++++++++---
 .../tests/main.nf.test.snap                   | 156 ++++++++++++-
 .../nf-core/picard/collectwgsmetrics/main.nf  |  30 +--
 .../nf-core/picard/collectwgsmetrics/meta.yml |   5 +-
 .../picard-collectwgsmetrics.diff             |  22 +-
 .../collectwgsmetrics/tests/main.nf.test      | 119 +++++++---
 .../collectwgsmetrics/tests/main.nf.test.snap |  74 +++++-
 modules/nf-core/star/align/meta.yml           |   6 -
 modules/nf-core/star/align/star-align.diff    |  20 +-
 modules/nf-core/star/align/tests/main.nf.test |  20 --
 nextflow.config                               |  12 +-
 nf-test.config                                |   2 +-
 ro-crate-metadata.json                        |  36 +--
 .../main.nf                                   |  95 ++++++++
 .../meta.yml                                  |   0
 .../fastq_align_dna/fastq_align_dna.diff      |   6 +-
 tests/config/igenomes_test.config             |   9 -
 tests/config/nf-test.config                   |   9 -
 tests/default.nf.test                         |  36 +--
 tests/inputs/fastq.yml                        |  22 --
 tests/inputs/fastq_rna.yml                    |  24 --
 tests/inputs/flowcell.yml                     |   6 -
 tests/inputs/test.yml                         |  51 ++++
 tests/workflows/preprocessing.nf.test         | 129 +++++++++--
 tests/workflows/preprocessing.nf.test.snap    | 187 +++++++++++----
 workflows/preprocessing.nf                    | 218 +++++++-----------
 70 files changed, 1999 insertions(+), 1170 deletions(-)
 delete mode 100644 modules/nf-core/bclconvert/bclconvert.diff
 delete mode 100644 modules/nf-core/multiqc/multiqc.diff
 create mode 100644 modules/nf-core/multiqcsav/environment.yml
 create mode 100644 modules/nf-core/multiqcsav/main.nf
 create mode 100644 modules/nf-core/multiqcsav/meta.yml
 create mode 100644 modules/nf-core/multiqcsav/tests/main.nf.test
 create mode 100644 modules/nf-core/multiqcsav/tests/main.nf.test.snap
 create mode 100644 subworkflows/local/utils_nfcmgg_preprocessing_pipeline/main.nf
 create mode 100644 subworkflows/local/utils_nfcmgg_preprocessing_pipeline/meta.yml
 delete mode 100644 tests/inputs/fastq.yml
 delete mode 100644 tests/inputs/fastq_rna.yml
 delete mode 100644 tests/inputs/flowcell.yml
 create mode 100644 tests/inputs/test.yml

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index e84f2948..d0f648f7 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -36,6 +36,6 @@ body:
         * Nextflow version _(eg. 23.04.0)_
         * Hardware _(eg. HPC, Desktop, Cloud)_
         * Executor _(eg. slurm, local, awsbatch)_
-        * Container engine: _(e.g. Docker, Singularity, Conda, Podman, Shifter, Charliecloud, or Apptainer)_
+        * Container engine: _(e.g. Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer)_
         * OS _(eg. CentOS Linux, macOS, Linux Mint)_
         * Version of nf-cmgg/preprocessing _(eg. 1.1, 1.5, 1.8.2)_
diff --git a/.github/actions/nf-test/action.yml b/.github/actions/nf-test/action.yml
index 3b9724c7..9c844d5a 100644
--- a/.github/actions/nf-test/action.yml
+++ b/.github/actions/nf-test/action.yml
@@ -46,16 +46,6 @@ runs:
         mkdir -p $NXF_SINGULARITY_CACHEDIR
         mkdir -p $NXF_SINGULARITY_LIBRARYDIR
 
-    - name: Conda setup
-      if: contains(inputs.profile, 'conda')
-      uses: conda-incubator/setup-miniconda@505e6394dae86d6a5c7fbb6e3fb8938e3e863830 # v3
-      with:
-        auto-update-conda: true
-        conda-solver: libmamba
-        channels: conda-forge
-        channel-priority: strict
-        conda-remove-defaults: true
-
     - name: Run nf-test
       shell: bash
       env:
diff --git a/.github/workflows/clean-up.yml b/.github/workflows/clean-up.yml
index 6adb0fff..587faa0b 100644
--- a/.github/workflows/clean-up.yml
+++ b/.github/workflows/clean-up.yml
@@ -12,9 +12,9 @@ jobs:
     steps:
       - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v10
         with:
-          stale-issue-message: "This issue has been tagged as awaiting-changes or awaiting-feedback by an nf-core contributor. Remove stale label or add a comment otherwise this issue will be closed in 20 days."
-          stale-pr-message: "This PR has been tagged as awaiting-changes or awaiting-feedback by an nf-core contributor. Remove stale label or add a comment if it is still useful."
-          close-issue-message: "This issue was closed because it has been tagged as awaiting-changes or awaiting-feedback by an nf-core contributor and then staled for 20 days with no activity."
+          stale-issue-message: "This issue has been tagged as awaiting-changes or awaiting-feedback by an nf-cmgg contributor. Remove stale label or add a comment otherwise this issue will be closed in 20 days."
+          stale-pr-message: "This PR has been tagged as awaiting-changes or awaiting-feedback by an nf-cmgg contributor. Remove stale label or add a comment if it is still useful."
+          close-issue-message: "This issue was closed because it has been tagged as awaiting-changes or awaiting-feedback by an nf-cmgg contributor and then staled for 20 days with no activity."
           days-before-stale: 30
           days-before-close: 20
           days-before-pr-close: -1
diff --git a/.github/workflows/nf-test.yml b/.github/workflows/nf-test.yml
index 2288a9b9..f55b0075 100644
--- a/.github/workflows/nf-test.yml
+++ b/.github/workflows/nf-test.yml
@@ -68,13 +68,11 @@ jobs:
       fail-fast: false
       matrix:
         shard: ${{ fromJson(needs.nf-test-changes.outputs.shard) }}
-        profile: [conda, docker, singularity]
+        profile: [docker, singularity]
         isMain:
           - ${{ github.base_ref == 'master' || github.base_ref == 'main' }}
-        # Exclude conda and singularity on dev
+        # Exclude singularity on dev
         exclude:
-          - isMain: false
-            profile: "conda"
           - isMain: false
             profile: "singularity"
         NXF_VER:
diff --git a/.gitignore b/.gitignore
index 23b0c7de..980c9ac3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,4 @@ testing*
 *.pyc
 null/
 .nf-test*
+test_fc
diff --git a/.nf-core.yml b/.nf-core.yml
index e1b35ad2..d06b88e6 100644
--- a/.nf-core.yml
+++ b/.nf-core.yml
@@ -35,4 +35,4 @@ template:
   org: nf-cmgg
   outdir: .
   skip_features: ["fastqc"]
-  version: 3.0.0dev
+  version: 3.0.0
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 18e37743..c062310d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## 3.0.0
 
+- Add `MultiQC-SAV` module for Illumina Run QC reports
+- Migrate readgroup parsing from `preprocessing.nf` to a local subworkflow
 - Update pipeline to use topic channels only, deprecation `versions` channels
 - Drop `bcl_demultiplex` subworkflow in favour of `bcl-convert` module
 - Update the output handling to use the new workflow output definitions.
diff --git a/README.md b/README.md
index f867fbba..119479bf 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,6 @@
 
 [![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.0-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)
 [![nf-core template version](https://img.shields.io/badge/nf--core_template-3.5.1-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.5.1)
-[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)
 [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)
 [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)
 [![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-cmgg/preprocessing)
diff --git a/assets/multiqc_config.yml b/assets/multiqc_config.yml
index cc354c76..3b59662f 100644
--- a/assets/multiqc_config.yml
+++ b/assets/multiqc_config.yml
@@ -1,6 +1,5 @@
 report_comment: >
-  This report has been generated by the <a href="https://github.com/nf-cmgg/preprocessing/tree/dev" target="_blank">nf-cmgg/preprocessing</a>
-  analysis pipeline.
+  This report has been generated by the <a href="https://github.com/nf-cmgg/preprocessing/releases/tag/3.0.0" target="_blank">nf-cmgg/preprocessing</a> analysis pipeline.
 report_section_order:
   "nf-cmgg-preprocessing-methods-description":
     order: -1000
diff --git a/conf/modules.config b/conf/modules.config
index f105629f..becf78e8 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -104,7 +104,7 @@ process {
     // -xf 2  : expansion factor for reading compressed data
 
     //// SNAP
-    withName: '.*FASTQ_ALIGN_DNA:SNAPALIGNER_ALIGN' {
+    withName: '.*FASTQ_ALIGN_DNA:SNAP_ALIGN' {
         cpus     = 16
         memory   = 64.GB
         ext.args = {
@@ -270,29 +270,27 @@ process {
     }
 
     // MultiQC
-    withName: '.*MULTIQC_.*$' {
-        container = "cmgg/multiqc_cmgg:0.0.2-multiqc-v1.33"
-        cpus      = 1
-        memory    = 4.GB
-    }
-    withName: '.*MULTIQC_MAIN' {
-        ext.prefix = { params.multiqc_title ? params.multiqc_title : "multiqc" }
+    withName: '.*MULTIQCSAV' {
+        cpus       = 1
+        memory     = 4.GB
+        ext.prefix = { params.multiqc_title ? "${params.multiqc_title}_SAV" : "${meta.id}_SAV" }
         ext.args   = {
             [
-                "--template \"cmgg\"",
                 "--no-ai",
-                params.multiqc_title ? "--title \"${params.multiqc_title}\"" : "",
+                params.multiqc_title ? "--title \"${params.multiqc_title}\"" : "--title \"${meta.id}\"",
             ].join(" ").trim()
         }
     }
-
-    withName: '.*MULTIQC_LIBRARY' {
-        ext.prefix = { meta.id ? "${meta.id}" : "multiqc_library" }
+    withName: '.*MULTIQC' {
+        container  = "cmgg/multiqc_cmgg:0.0.5-multiqc-v1.33"
+        cpus       = 1
+        memory     = 4.GB
+        ext.prefix = { params.multiqc_title ? "${params.multiqc_title}_${meta.id}" : "${meta.id}" }
         ext.args   = {
             [
                 "--template \"cmgg\"",
                 "--no-ai",
-                meta.id ? "--title \"${meta.id} - Pool Summary\"" : "",
+                params.multiqc_title ? "--title \"${params.multiqc_title} - ${meta.id}\"" : "--title \"${meta.id}\"",
             ].join(" ").trim()
         }
     }
diff --git a/conf/test.config b/conf/test.config
index f4ad2b2b..8b6c7486 100644
--- a/conf/test.config
+++ b/conf/test.config
@@ -15,7 +15,7 @@ params {
     config_profile_description = 'Minimal test dataset to check pipeline function'
 
     // Input data
-    input                      = "${projectDir}/tests/inputs/fastq.yml"
+    input                      = "${projectDir}/tests/inputs/test.yml"
     igenomes_base              = "s3://reference-data/genomes"
 }
 
@@ -25,16 +25,6 @@ process {
         memory: 6.GB,
         time: 6.h,
     ]
-
-    withName: BCLCONVERT {
-        ext.args = {
-            [
-                meta.lane ? "--bcl-only-lane ${meta.lane}" : "",
-                "--force",
-                "--first-tile-only true",
-            ].join(" ").trim()
-        }
-    }
 }
 
 includeConfig "../tests/config/igenomes_test.config"
diff --git a/docs/usage.md b/docs/usage.md
index 1f1d72d2..32028ecd 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -51,7 +51,7 @@ Following table shows the fields that are used by the `fastq` samplesheet:
 | `roi`        | The path to a BED file containing <b>R</b>egions <b>O</b>f <b>I</b>nterest for coverage analysis                                             | :x:                                             |
 | `aligner`    | The aligner to use for this sample. Can be one of these: bowtie2, bwamem, bwamem2, dragmap, strobe and snap. set to `false` to output fastq. | :x:                                             |
 
-An [example samplesheet](../tests/inputs/fastq.yml) has been provided with the pipeline.
+An [example samplesheet](../tests/inputs/test.yml) has been provided with the pipeline.
 
 ### Flowcell samplesheet
 
@@ -64,22 +64,26 @@ RUN_NAME,RUN_NAME_samplesheet.csv,RUN_NAME_sampleinfo.csv,RUN_NAME_flowcell/
 
 Following table shows the fields that are used by the `flowcell` samplesheet:
 
-| Column        | Description                                                                                            | Required           |
-| ------------- | ------------------------------------------------------------------------------------------------------ | ------------------ |
-| `samplesheet` | Illumina flowcell for the flowcell lane                                                                | :heavy_check_mark: |
-| `sample_info` | CSV file with sample information. See the [flowcell sample info](#flowcell-sample-info) documentation. | :heavy_check_mark: |
-| `flowcell`    | Illumina flowcell directory                                                                            | :heavy_check_mark: |
-| `lane`        | FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'           | :x:                |
+| Column        | Description                                                                                                 | Required           |
+| ------------- | ----------------------------------------------------------------------------------------------------------- | ------------------ |
+| `samplesheet` | Illumina flowcell for the flowcell lane                                                                     | :heavy_check_mark: |
+| `sample_info` | JSON/YML file with sample information. See the [flowcell sample info](#flowcell-sample-info) documentation. | :heavy_check_mark: |
+| `flowcell`    | Illumina flowcell directory                                                                                 | :heavy_check_mark: |
+| `lane`        | Lane number                                                                                                 | :x:                |
 
-An [example samplesheet](../tests/inputs/flowcell.yml) has been provided with the pipeline.
+An [example samplesheet](../tests/inputs/test.yml) has been provided with the pipeline.
 
 ### Flowcell sample info
 
-A `flowcell` sample info CSV file consisting for one sequencing run may look something like the one below.
+A `flowcell` sample info JSON/YML file consisting for one sequencing run may look something like the one below.
 
-```csv title="sample_info.csv"
-samplename,library,organism,tag
-Sample1,test,Homo sapiens,WES
+```json title="sample_info.json"
+{
+  "samplename": "Sample1",
+  "library": "test",
+  "organism": "Homo sapiens",
+  "tag": "WES"
+}
 ```
 
 Following table shows the fields that are used by the `flowcell` samplesheet:
@@ -182,11 +186,7 @@ These options are part of Nextflow and use a _single_ hyphen (pipeline parameter
 
 Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.
 
-Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.
-
-:::info
-We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.
-:::
+Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer) - see below.
 
 The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation).
 
@@ -210,8 +210,6 @@ If `-profile` is not specified, the pipeline will run locally and expect all sof
   - A generic configuration profile to be used with [Charliecloud](https://hpc.github.io/charliecloud/)
 - `apptainer`
   - A generic configuration profile to be used with [Apptainer](https://apptainer.org/)
-- `conda`
-  - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
 
 ### `-resume`
 
@@ -233,9 +231,9 @@ To change the resource requests, please see the [max resources](https://nf-co.re
 
 ### Custom Containers
 
-In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However in some cases the pipeline specified version maybe out of date.
+In some cases you may wish to change which container a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However in some cases the pipeline specified version maybe out of date.
 
-To use a different container from the default container or conda environment specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/usage/configuration#updating-tool-versions) section of the nf-core website.
+To use a different container from the default container specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/usage/configuration#updating-tool-versions) section of the nf-core website.
 
 ### Custom Tool Arguments
 
diff --git a/main.nf b/main.nf
index aed6d7b5..e2f78c8b 100644
--- a/main.nf
+++ b/main.nf
@@ -46,6 +46,11 @@ workflow {
         PIPELINE_INITIALISATION.out.samplesheet,
         params.genomes,
         params.genelists,
+        params.multiqc_config
+            ? [file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true), file(params.multiqc_config, checkIfExists: true)]
+            : [file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true)],
+        params.multiqc_logo ? file(params.multiqc_logo, checkIfExists: true) : [],
+        params.multiqc_methods_description ? file(params.multiqc_methods_description, checkIfExists: true) : file("${projectDir}/assets/methods_description_template.yml", checkIfExists: true),
     )
 
     //
@@ -58,14 +63,14 @@ workflow {
         params.outdir,
         params.monochrome_logs,
         params.hook_url,
-        PREPROCESSING.out.multiqc_main_report,
+        PREPROCESSING.out.multiqc_report,
     )
 
     publish:
-    demultiplex_interop        = PREPROCESSING.out.demultiplex_interop.transpose(by: 1)
-    demultiplex_reports        = PREPROCESSING.out.demultiplex_reports.transpose(by: 1)
-    demultiplex_logs           = PREPROCESSING.out.demultiplex_logs.transpose(by: 1)
+    demultiplex_reports        = PREPROCESSING.out.demultiplex_reports.transpose()
+    demultiplex_logs           = PREPROCESSING.out.demultiplex_logs.transpose()
     demultiplex_fastq          = PREPROCESSING.out.demultiplex_fastq.transpose()
+    demultiplex_interop        = PREPROCESSING.out.demultiplex_interop.transpose()
     falco_html                 = PREPROCESSING.out.falco_html
     falco_txt                  = PREPROCESSING.out.falco_txt
     fastp_json                 = PREPROCESSING.out.fastp_json
@@ -97,242 +102,211 @@ workflow {
     picard_wgsmetrics          = PREPROCESSING.out.picard_wgsmetrics
     picard_hsmetrics           = PREPROCESSING.out.picard_hsmetrics
     md5sums                    = PREPROCESSING.out.md5sums
-    multiqc_main_report        = PREPROCESSING.out.multiqc_main_report
-    multiqc_main_data          = PREPROCESSING.out.multiqc_main_data
-    multiqc_main_plots         = PREPROCESSING.out.multiqc_main_plots
-    multiqc_library_report     = PREPROCESSING.out.multiqc_library_report
-    multiqc_library_data       = PREPROCESSING.out.multiqc_library_data
-    multiqc_library_plots      = PREPROCESSING.out.multiqc_library_plots
+    multiqc_report             = PREPROCESSING.out.multiqc_report
+    multiqc_data               = PREPROCESSING.out.multiqc_data
+    multiqc_plots              = PREPROCESSING.out.multiqc_plots
+    multiqcsav_report          = PREPROCESSING.out.multiqcsav_report
+    multiqcsav_data            = PREPROCESSING.out.multiqcsav_data
+    multiqcsav_plots           = PREPROCESSING.out.multiqcsav_plots
 }
 
 output {
-    demultiplex_interop {
-        path { _meta, bin ->
-            bin >> "Interop/${bin.name}"
-        }
-    }
     demultiplex_reports {
         path { meta, report ->
-            def out_path = meta.lane ? "Reports/L00${meta.lane}/${report.name}" as String : "Reports/${report.name}"
-            report >> out_path
+            report >> (meta.lane ? "Reports/L00${meta.lane}/${report.name}" : "Reports/${report.name}")
         }
     }
     demultiplex_logs {
         path { meta, log ->
-            def out_path = meta.lane ? "Logs/L00${meta.lane}/${log.name}" as String : "Logs/${log.name}"
-            log >> out_path
+            log >> (meta.lane ? "Logs/L00${meta.lane}/${log.name}" : "Logs/${log.name}")
         }
     }
+    demultiplex_interop {
+        path "InterOp/"
+    }
     demultiplex_fastq {
         path { meta, fastq ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${fastq.name}" as String : "${meta.samplename}/${fastq.name}"
-            fastq >> out_path
+            fastq >> (meta.library ? "${meta.library}/${meta.samplename}/${fastq.name}" : "${meta.samplename}/${fastq.name}")
         }
     }
     falco_html {
         path { meta, html ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${html.name}" as String : "${meta.samplename}/${html.name}"
-            html >> out_path
+            html >> (meta.library ? "${meta.library}/${meta.samplename}/${html.name}" : "${meta.samplename}/${html.name}")
         }
     }
     falco_txt {
         path { meta, txt ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${txt.name}" as String : "${meta.samplename}/${txt.name}"
-            txt >> out_path
+            txt >> (meta.library ? "${meta.library}/${meta.samplename}/${txt.name}" : "${meta.samplename}/${txt.name}")
         }
     }
     fastp_json {
         path { meta, json ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${json.name}" as String : "${meta.samplename}/${json.name}"
-            json >> out_path
+            json >> (meta.library ? "${meta.library}/${meta.samplename}/${json.name}" : "${meta.samplename}/${json.name}")
         }
     }
     fastp_html {
         path { meta, html ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${html.name}" as String : "${meta.samplename}/${html.name}"
-            html >> out_path
+            html >> (meta.library ? "${meta.library}/${meta.samplename}/${html.name}" : "${meta.samplename}/${html.name}")
         }
     }
     crams {
         path { meta, cram, crai ->
-            def out_cram = meta.library ? "${meta.library}/${meta.samplename}/${meta.samplename}.cram" as String : "${meta.samplename}/${meta.samplename}.cram"
-            def out_crai = meta.library ? "${meta.library}/${meta.samplename}/${meta.samplename}.cram.crai" as String : "${meta.samplename}/${meta.samplename}.cram.crai"
-            cram >> out_cram
-            crai >> out_crai
+            cram >> (meta.library ? "${meta.library}/${meta.samplename}/${meta.samplename}.cram" : "${meta.samplename}/${meta.samplename}.cram")
+            crai >> (meta.library ? "${meta.library}/${meta.samplename}/${meta.samplename}.cram.crai" : "${meta.samplename}/${meta.samplename}.cram.crai")
         }
     }
     rna_splice_junctions {
         path { meta, sjt ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${sjt.name}" as String : "${meta.samplename}/${sjt.name}"
-            sjt >> out_path
+            sjt >> (meta.library ? "${meta.library}/${meta.samplename}/${sjt.name}" : "${meta.samplename}/${sjt.name}")
         }
     }
     rna_junctions {
         path { meta, junctions ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${junctions.name}" as String : "${meta.samplename}/${junctions.name}"
-            junctions >> out_path
+            junctions >> (meta.library ? "${meta.library}/${meta.samplename}/${junctions.name}" : "${meta.samplename}/${junctions.name}")
         }
     }
     align_reports {
         path { meta, log ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${log.name}" as String : "${meta.samplename}/${log.name}"
-            log >> out_path
+            log >> (meta.library ? "${meta.library}/${meta.samplename}/${log.name}" : "${meta.samplename}/${log.name}")
         }
     }
     sormadup_metrics {
         path { meta, metrics ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/${meta.samplename}.duplicate_metrics.txt" as String : "${meta.samplename}/${meta.samplename}.duplicate_metrics.txt"
-            metrics >> out_path
+            metrics >> (meta.library ? "${meta.library}/${meta.samplename}/${meta.samplename}.duplicate_metrics.txt" : "${meta.samplename}/${meta.samplename}.duplicate_metrics.txt")
         }
     }
     mosdepth_global {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_summary {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_regions {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_per_base_d4 {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_per_base_bed {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_per_base_csi {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_regions_bed {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_regions_csi {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_quantized_bed {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_quantized_csi {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_thresholds_bed {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     mosdepth_thresholds_csi {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     samtools_coverage {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     panelcoverage {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     samtools_stats {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     samtools_flagstat {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     samtools_idxstats {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     picard_multiplemetrics {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     picard_multiplemetrics_pdf {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     picard_wgsmetrics {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     picard_hsmetrics {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
     md5sums {
         path { meta, _file ->
-            def out_path = meta.library ? "${meta.library}/${meta.samplename}/" as String : "${meta.samplename}/"
-            return out_path
+            return (meta.library ? "${meta.library}/${meta.samplename}/" : "${meta.samplename}/")
         }
     }
-    multiqc_main_report {
+    multiqcsav_report {
         path "multiqc/"
     }
-    multiqc_main_data {
+    multiqcsav_data {
         path "multiqc/"
     }
-    multiqc_main_plots {
+    multiqcsav_plots {
         path "multiqc/"
     }
-    multiqc_library_report {
-        path "multiqc/"
+    multiqc_report {
+        path { meta, _file ->
+            return (meta.id ? "${meta.id}/multiqc/" : "multiqc/")
+        }
     }
-    multiqc_library_data {
-        path "multiqc/"
+    multiqc_data {
+        path { meta, _file ->
+            return (meta.id ? "${meta.id}/multiqc/" : "multiqc/")
+        }
     }
-    multiqc_library_plots {
-        path "multiqc/"
+    multiqc_plots {
+        path { meta, _file ->
+            return (meta.id ? "${meta.id}/multiqc/" : "multiqc/")
+        }
     }
 }
diff --git a/modules.json b/modules.json
index 6d2aefc7..410a4a68 100644
--- a/modules.json
+++ b/modules.json
@@ -7,9 +7,8 @@
                 "nf-core": {
                     "bclconvert": {
                         "branch": "master",
-                        "git_sha": "18e1a9566d76569b8a40dc60184d4eb7413cf6e1",
-                        "installed_by": ["modules"],
-                        "patch": "modules/nf-core/bclconvert/bclconvert.diff"
+                        "git_sha": "e4a7e011bee58ded839f4da6bf9bb3fbfbebdebd",
+                        "installed_by": ["modules"]
                     },
                     "biobambam/bamsormadup": {
                         "branch": "master",
@@ -19,7 +18,7 @@
                     },
                     "bowtie2/align": {
                         "branch": "master",
-                        "git_sha": "ab146e7909edbf6dcc6459de57eef29dceb61d42",
+                        "git_sha": "92b8df948fd8cdb223e051f5f5e414818a073ee0",
                         "installed_by": ["fastq_align_dna", "modules"],
                         "patch": "modules/nf-core/bowtie2/align/bowtie2-align.diff"
                     },
@@ -31,7 +30,7 @@
                     },
                     "bwamem2/mem": {
                         "branch": "master",
-                        "git_sha": "5dd46a36fca68d6ad1a6b22ec47adc8c6863717d",
+                        "git_sha": "8325a8155a77a336a613a504b8e4d6cea7a2344a",
                         "installed_by": ["fastq_align_dna"],
                         "patch": "modules/nf-core/bwamem2/mem/bwamem2-mem.diff"
                     },
@@ -69,25 +68,30 @@
                     },
                     "multiqc": {
                         "branch": "master",
-                        "git_sha": "575e1a4b51a9bad7a8cd1316a88fb85684ef7c7b",
+                        "git_sha": "2c73cc8fa92cf48de3da0b643fdf357a8a290b36",
                         "installed_by": ["modules"],
                         "patch": "modules/nf-core/multiqc/multiqc.diff"
                     },
+                    "multiqcsav": {
+                        "branch": "master",
+                        "git_sha": "2c73cc8fa92cf48de3da0b643fdf357a8a290b36",
+                        "installed_by": ["modules"]
+                    },
                     "picard/collecthsmetrics": {
                         "branch": "master",
-                        "git_sha": "976ed20e328a92cb24ab6c63a8983ed31bf48469",
+                        "git_sha": "a631e12055f6c23ba2c942d3902b3ed1b9eed859",
                         "installed_by": ["modules"],
                         "patch": "modules/nf-core/picard/collecthsmetrics/picard-collecthsmetrics.diff"
                     },
                     "picard/collectmultiplemetrics": {
                         "branch": "master",
-                        "git_sha": "74ec93d00bef147da3fb1f2262e8d31c14108f88",
+                        "git_sha": "a631e12055f6c23ba2c942d3902b3ed1b9eed859",
                         "installed_by": ["modules"],
                         "patch": "modules/nf-core/picard/collectmultiplemetrics/picard-collectmultiplemetrics.diff"
                     },
                     "picard/collectwgsmetrics": {
                         "branch": "master",
-                        "git_sha": "66d5808eaaabd9de8997c4c31a9e8cdd3b56c080",
+                        "git_sha": "a631e12055f6c23ba2c942d3902b3ed1b9eed859",
                         "installed_by": ["modules"],
                         "patch": "modules/nf-core/picard/collectwgsmetrics/picard-collectwgsmetrics.diff"
                     },
@@ -139,7 +143,7 @@
                     },
                     "star/align": {
                         "branch": "master",
-                        "git_sha": "d6419d592de78e193625b209c1d0a5cc09206df3",
+                        "git_sha": "cebe21bbd158c15c8fab172e37cfe97a239f4b77",
                         "installed_by": ["modules"],
                         "patch": "modules/nf-core/star/align/star-align.diff"
                     },
@@ -155,7 +159,7 @@
                 "nf-core": {
                     "fastq_align_dna": {
                         "branch": "master",
-                        "git_sha": "51cf05a850f58964b0d12ab92230348e567819e3",
+                        "git_sha": "9afa0584136287aa20fc18296f45f103c0c4e69a",
                         "installed_by": ["subworkflows"],
                         "patch": "subworkflows/nf-core/fastq_align_dna/fastq_align_dna.diff"
                     },
diff --git a/modules/nf-core/bclconvert/bclconvert.diff b/modules/nf-core/bclconvert/bclconvert.diff
deleted file mode 100644
index e850ef91..00000000
--- a/modules/nf-core/bclconvert/bclconvert.diff
+++ /dev/null
@@ -1,51 +0,0 @@
-Changes in component 'nf-core/bclconvert'
-'modules/nf-core/bclconvert/LICENSE' is unchanged
-'modules/nf-core/bclconvert/Dockerfile' is unchanged
-'modules/nf-core/bclconvert/README.md' is unchanged
-'modules/nf-core/bclconvert/.gitignore' is unchanged
-'modules/nf-core/bclconvert/meta.yml' is unchanged
-Changes in 'bclconvert/main.nf':
---- modules/nf-core/bclconvert/main.nf
-+++ modules/nf-core/bclconvert/main.nf
-@@ -103,3 +103,36 @@
-     echo "fake InterOp file" > output/InterOp/TileMetricsOut.bin
-     """
- }
-+
-+def generateReadgroup(ch_fastq_list_csv, ch_fastq) {
-+    return ch_fastq_list_csv
-+        .join(ch_fastq, by: [0])
-+        .map { meta, csv_file, fastq_list ->
-+            def meta_fastq = []
-+            csv_file
-+                .splitCsv(header: true)
-+                .each { row ->
-+                    // Create the readgroup tuple
-+                    // RGID,RGSM,RGLB,Lane,Read1File,Read2File
-+                    def rg = [:]
-+                    // row.RGID is index1.index2.lane
-+                    rg.ID = row.RGID
-+                    // RGPU is a custom column in the samplesheet containing the flowcell ID
-+                    rg.PU = row.RGPU ? row.RGPU : meta.id + "." + row.Lane
-+                    rg.SM = row.RGSM
-+                    rg.LB = row.RGLB ? row.RGLB : ""
-+                    rg.PL = "ILLUMINA"
-+
-+                    // dereference the fastq files in the csv
-+                    def fastq1 = fastq_list.find { fq -> file(fq).name == file(row.Read1File).name }
-+                    def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
-+
-+                    // set fastq metadata
-+                    def new_meta = meta + [id: fastq1.getSimpleName().toString() - ~/_R[0-9]_001.*$/, readgroup: rg, single_end: !fastq2]
-+
-+                    meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
-+                }
-+            return meta_fastq
-+        }
-+        .flatMap()
-+}
-
-'modules/nf-core/bclconvert/tests/main.nf.test.snap' is unchanged
-'modules/nf-core/bclconvert/tests/nextflow.config' is unchanged
-'modules/nf-core/bclconvert/tests/main.nf.test' is unchanged
-************************************************************
diff --git a/modules/nf-core/bclconvert/main.nf b/modules/nf-core/bclconvert/main.nf
index 7010ce8a..1b83e5df 100644
--- a/modules/nf-core/bclconvert/main.nf
+++ b/modules/nf-core/bclconvert/main.nf
@@ -12,8 +12,8 @@ process BCLCONVERT {
     tuple val(meta), path("output/**_S[1-9]*_I?_00?.fastq.gz"), emit: fastq_idx, optional: true
     tuple val(meta), path("output/**Undetermined_S0*_R?_00?.fastq.gz"), emit: undetermined, optional: true
     tuple val(meta), path("output/**Undetermined_S0*_I?_00?.fastq.gz"), emit: undetermined_idx, optional: true
-    tuple val(meta), path("output/Reports/*.{csv,xml,bin}"), emit: reports
-    tuple val(meta), path("output/Logs/*.{log,txt}"), emit: logs
+    tuple val(meta), path("output/Reports"), emit: reports
+    tuple val(meta), path("output/Logs"), emit: logs
     tuple val(meta), path("output/InterOp/*.bin"), emit: interop, optional: true
     tuple val("${task.process}"), val('bclconvert'), eval("bcl-convert -V 2>&1 | head -n 1 | sed 's/^.*Version //'"), topic: versions, emit: versions_bclconvert
 
@@ -103,36 +103,3 @@ process BCLCONVERT {
     echo "fake InterOp file" > output/InterOp/TileMetricsOut.bin
     """
 }
-
-def generateReadgroup(ch_fastq_list_csv, ch_fastq) {
-    return ch_fastq_list_csv
-        .join(ch_fastq, by: [0])
-        .map { meta, csv_file, fastq_list ->
-            def meta_fastq = []
-            csv_file
-                .splitCsv(header: true)
-                .each { row ->
-                    // Create the readgroup tuple
-                    // RGID,RGSM,RGLB,Lane,Read1File,Read2File
-                    def rg = [:]
-                    // row.RGID is index1.index2.lane
-                    rg.ID = row.RGID
-                    // RGPU is a custom column in the samplesheet containing the flowcell ID
-                    rg.PU = row.RGPU ? row.RGPU : meta.id + "." + row.Lane
-                    rg.SM = row.RGSM
-                    rg.LB = row.RGLB ? row.RGLB : ""
-                    rg.PL = "ILLUMINA"
-
-                    // dereference the fastq files in the csv
-                    def fastq1 = fastq_list.find { fq -> file(fq).name == file(row.Read1File).name }
-                    def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
-
-                    // set fastq metadata
-                    def new_meta = meta + [id: fastq1.getSimpleName().toString() - ~/_R[0-9]_001.*$/, readgroup: rg, single_end: !fastq2]
-
-                    meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
-                }
-            return meta_fastq
-        }
-        .flatMap()
-}
diff --git a/modules/nf-core/bclconvert/meta.yml b/modules/nf-core/bclconvert/meta.yml
index a483715a..27f9b1db 100644
--- a/modules/nf-core/bclconvert/meta.yml
+++ b/modules/nf-core/bclconvert/meta.yml
@@ -75,10 +75,10 @@ output:
     - - meta:
           type: map
           description: Groovy Map containing sample information
-      - output/Reports/*.{csv,xml,bin}:
+      - output/Reports:
           type: file
           description: Demultiplexing Reports
-          pattern: "Reports/*.{csv,xml,bin}"
+          pattern: "Reports"
           ontologies:
             - edam: http://edamontology.org/format_3752 #CSV
             - edam: http://edamontology.org/format_2332 #XML
@@ -87,10 +87,10 @@ output:
     - - meta:
           type: map
           description: Groovy Map containing sample information
-      - output/Logs/*.{log,txt}:
+      - output/Logs:
           type: file
           description: Demultiplexing Logs
-          pattern: "Logs/*.{log,txt}"
+          pattern: "Logs"
           ontologies:
             - edam: http://edamontology.org/format_2330 #TEXT
   interop:
diff --git a/modules/nf-core/bclconvert/tests/main.nf.test b/modules/nf-core/bclconvert/tests/main.nf.test
index 56ec207a..b1b7acab 100644
--- a/modules/nf-core/bclconvert/tests/main.nf.test
+++ b/modules/nf-core/bclconvert/tests/main.nf.test
@@ -33,8 +33,8 @@ nextflow_process {
                     process.out.fastq_idx,
                     process.out.undetermined.collect { meta, fastq -> file(fastq).name },
                     process.out.undetermined_idx,
-                    process.out.reports.collect {meta, files -> files.collect { file(it).name }.sort() },
-                    process.out.logs.collect {meta, files -> files.collect { file(it).name }.sort() },
+                    process.out.reports.collect {meta, dir -> file(dir).name },
+                    process.out.logs.collect {meta, dir -> file(dir).name },
                     process.out.interop.collect {meta, files -> files.collect { file(it).name }.sort() },
                     process.out.findAll { key, val -> key.startsWith("versions") }
                 ).match() }
@@ -72,8 +72,8 @@ nextflow_process {
                     process.out.fastq_idx,
                     process.out.undetermined.collect { meta, fastq -> file(fastq).name },
                     process.out.undetermined_idx,
-                    process.out.reports.collect {meta, files -> files.collect { file(it).name }.sort() },
-                    process.out.logs.collect {meta, files -> files.collect { file(it).name }.sort() },
+                    process.out.reports.collect {meta, dir -> file(dir).name },
+                    process.out.logs.collect {meta, dir -> file(dir).name },
                     process.out.interop.collect {meta, files -> files.collect { file(it).name }.sort() },
                     process.out.findAll { key, val -> key.startsWith("versions") }
                 ).match() }
diff --git a/modules/nf-core/bclconvert/tests/main.nf.test.snap b/modules/nf-core/bclconvert/tests/main.nf.test.snap
index 43233d59..b228a5b2 100644
--- a/modules/nf-core/bclconvert/tests/main.nf.test.snap
+++ b/modules/nf-core/bclconvert/tests/main.nf.test.snap
@@ -19,29 +19,10 @@
                 
             ],
             [
-                [
-                    "Adapter_Cycle_Metrics.csv",
-                    "Adapter_Metrics.csv",
-                    "Demultiplex_Detailed_Stats.csv",
-                    "Demultiplex_Stats.csv",
-                    "Demultiplex_Tile_Stats.csv",
-                    "IndexMetricsOut.bin",
-                    "Index_Hopping_Counts.csv",
-                    "Quality_Metrics.csv",
-                    "Quality_Tile_Metrics.csv",
-                    "RunInfo.xml",
-                    "SampleSheet.csv",
-                    "Top_Unknown_Barcodes.csv",
-                    "fastq_list.csv"
-                ]
+                "Reports"
             ],
             [
-                [
-                    "Errors.log",
-                    "FastqComplete.txt",
-                    "Info.log",
-                    "Warnings.log"
-                ]
+                "Logs"
             ],
             [
                 [
@@ -77,7 +58,7 @@
             "nf-test": "0.9.3",
             "nextflow": "25.10.4"
         },
-        "timestamp": "2026-02-17T16:59:36.617127"
+        "timestamp": "2026-03-04T20:03:26.54479"
     },
     "homo_sapiens illumina [bcl] - stub": {
         "content": [
@@ -279,29 +260,10 @@
                 
             ],
             [
-                [
-                    "Adapter_Cycle_Metrics.csv",
-                    "Adapter_Metrics.csv",
-                    "Demultiplex_Detailed_Stats.csv",
-                    "Demultiplex_Stats.csv",
-                    "Demultiplex_Tile_Stats.csv",
-                    "IndexMetricsOut.bin",
-                    "Index_Hopping_Counts.csv",
-                    "Quality_Metrics.csv",
-                    "Quality_Tile_Metrics.csv",
-                    "RunInfo.xml",
-                    "SampleSheet.csv",
-                    "Top_Unknown_Barcodes.csv",
-                    "fastq_list.csv"
-                ]
+                "Reports"
             ],
             [
-                [
-                    "Errors.log",
-                    "FastqComplete.txt",
-                    "Info.log",
-                    "Warnings.log"
-                ]
+                "Logs"
             ],
             [
                 [
@@ -328,6 +290,6 @@
             "nf-test": "0.9.3",
             "nextflow": "25.10.4"
         },
-        "timestamp": "2026-02-17T16:59:10.205948"
+        "timestamp": "2026-03-04T20:02:51.80369"
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/bowtie2/align/main.nf b/modules/nf-core/bowtie2/align/main.nf
index 86aa8bef..36383249 100644
--- a/modules/nf-core/bowtie2/align/main.nf
+++ b/modules/nf-core/bowtie2/align/main.nf
@@ -31,6 +31,7 @@ process BOWTIE2_ALIGN {
     def args = task.ext.args ?: ""
     def args2 = task.ext.args2 ?: ""
     def prefix = task.ext.prefix ?: "${meta.id}"
+    def rg = args.contains("--rg-id") ? "" : "--rg-id ${prefix} --rg SM:${prefix}"
 
     def unaligned = ""
     def reads_args = ""
@@ -59,6 +60,7 @@ process BOWTIE2_ALIGN {
         $reads_args \\
         --threads $task.cpus \\
         $unaligned \\
+        $rg \\
         $args \\
         2>| >(tee ${prefix}.bowtie2.log >&2) \\
         | samtools $samtools_command $args2 --threads $task.cpus ${reference} -o ${prefix}.${extension} -
diff --git a/modules/nf-core/bowtie2/align/tests/main.nf.test.snap b/modules/nf-core/bowtie2/align/tests/main.nf.test.snap
index c8c6d4b5..b1df41e7 100644
--- a/modules/nf-core/bowtie2/align/tests/main.nf.test.snap
+++ b/modules/nf-core/bowtie2/align/tests/main.nf.test.snap
@@ -47,11 +47,11 @@
     "sarscov2 - fastq, index, fasta, false, false - sam2": {
         "content": [
             [
-                "ERR5069949.2151832\t16\tMT192765.1\t17453\t42\t150M\t*\t0\t0\tACGCACATTGCTAACTAAGGGCACACTAGAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGTCCTGCTGAAATTGTTGACACTGTGAGTGCTTTGGTTTATGA\tAAAA<EEEEEEAEEEAEAAAAEEEEEEEEEAAAEE<EEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:150\tYT:Z:UU",
-                "ERR5069949.576388\t16\tMT192765.1\t5798\t42\t77M\t*\t0\t0\tGCATAGACGGTGCTTTACTTACAAAGTCCTCAGAATACAAAGGTCCTATTACGGATGTTTTCTACAAAGAAAACAGT\tEA/AEEE/<EEEEEEEEEEEAA<EEEEEEEEEEEEEEEEEEEEEAEEEEEAEEEAEE6/EEEAEEEEEEEEEA6AAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:77\tYT:Z:UU",
-                "ERR5069949.501486\t16\tMT192765.1\t5423\t42\t146M\t*\t0\t0\tTAGGTGAGTTAGGTGATGTTAGAGAAACAATGAGTTACTTGTTTCAACATGCCAATTTAGATTCTTGCAAAAGAGTCTTGAACGTGGTGTGTAAAACTTGTGGACAACAGCAGACAACCCTTAAGGGTGTAGAAGCTGTTATGTAC\tEAAAAEAEEEE6E<AEEEEEEEE<EEEEEEAAEE/EEEEEE/<EEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEEEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:146\tYT:Z:UU",
-                "ERR5069949.1331889\t0\tMT192765.1\t13010\t42\t132M\t*\t0\t0\tGTCTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAATTCAACTGTATTATCTTTCTGTGCTTTTGCTGTAGATGCTGCTAAAGCTTACAAAGATTATCTAGCTAGTGGGGGACAACCAATCACTAATTGTG\tA/AAAEEEEEEEEEEEEEEEEEEEAEEEEAEEEEEEEEEEEAEEEEEEEEEEEEEEE/EEEEE<AEAEEEEE/EAEAEEE/AEEEEEEEEEEEEEEEEEEEEAE/EEEEEEEEEEEEEEEEEEEEEEEA<EE\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:132\tYT:Z:UU",
-                "ERR5069949.2161340\t0\tMT192765.1\t17482\t42\t80M\t*\t0\t0\tAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGT\tA/AA//EEAEA/E/AEEEE6EE/EEEA/6AEEEEEEEEE6EEEAEAEE//A/EEEEEE//E/E/A//E/E/<<EE</E/E\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:80\tYT:Z:UU"
+                "ERR5069949.2151832\t16\tMT192765.1\t17453\t42\t150M\t*\t0\t0\tACGCACATTGCTAACTAAGGGCACACTAGAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGTCCTGCTGAAATTGTTGACACTGTGAGTGCTTTGGTTTATGA\tAAAA<EEEEEEAEEEAEAAAAEEEEEEEEEAAAEE<EEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:150\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.576388\t16\tMT192765.1\t5798\t42\t77M\t*\t0\t0\tGCATAGACGGTGCTTTACTTACAAAGTCCTCAGAATACAAAGGTCCTATTACGGATGTTTTCTACAAAGAAAACAGT\tEA/AEEE/<EEEEEEEEEEEAA<EEEEEEEEEEEEEEEEEEEEEAEEEEEAEEEAEE6/EEEAEEEEEEEEEA6AAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:77\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.501486\t16\tMT192765.1\t5423\t42\t146M\t*\t0\t0\tTAGGTGAGTTAGGTGATGTTAGAGAAACAATGAGTTACTTGTTTCAACATGCCAATTTAGATTCTTGCAAAAGAGTCTTGAACGTGGTGTGTAAAACTTGTGGACAACAGCAGACAACCCTTAAGGGTGTAGAAGCTGTTATGTAC\tEAAAAEAEEEE6E<AEEEEEEEE<EEEEEEAAEE/EEEEEE/<EEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEEEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:146\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.1331889\t0\tMT192765.1\t13010\t42\t132M\t*\t0\t0\tGTCTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAATTCAACTGTATTATCTTTCTGTGCTTTTGCTGTAGATGCTGCTAAAGCTTACAAAGATTATCTAGCTAGTGGGGGACAACCAATCACTAATTGTG\tA/AAAEEEEEEEEEEEEEEEEEEEAEEEEAEEEEEEEEEEEAEEEEEEEEEEEEEEE/EEEEE<AEAEEEEE/EAEAEEE/AEEEEEEEEEEEEEEEEEEEEAE/EEEEEEEEEEEEEEEEEEEEEEEA<EE\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:132\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.2161340\t0\tMT192765.1\t17482\t42\t80M\t*\t0\t0\tAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGT\tA/AA//EEAEA/E/AEEEE6EE/EEEA/6AEEEEEEEEE6EEEAEAEE//A/EEEEEE//E/E/A//E/E/<<EE</E/E\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:80\tYT:Z:UU\tRG:Z:test"
             ],
             [
                 [
@@ -91,9 +91,9 @@
         ],
         "meta": {
             "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
+            "nextflow": "25.10.2"
         },
-        "timestamp": "2026-02-03T15:17:39.204319466"
+        "timestamp": "2026-02-25T09:58:55.356484"
     },
     "sarscov2 - [fastq1, fastq2], index, fasta, true, true - cram": {
         "content": [
@@ -410,11 +410,11 @@
     "sarscov2 - fastq, index, fasta, false, false - sam": {
         "content": [
             [
-                "ERR5069949.2151832\t16\tMT192765.1\t17453\t42\t150M\t*\t0\t0\tACGCACATTGCTAACTAAGGGCACACTAGAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGTCCTGCTGAAATTGTTGACACTGTGAGTGCTTTGGTTTATGA\tAAAA<EEEEEEAEEEAEAAAAEEEEEEEEEAAAEE<EEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:150\tYT:Z:UU",
-                "ERR5069949.576388\t16\tMT192765.1\t5798\t42\t77M\t*\t0\t0\tGCATAGACGGTGCTTTACTTACAAAGTCCTCAGAATACAAAGGTCCTATTACGGATGTTTTCTACAAAGAAAACAGT\tEA/AEEE/<EEEEEEEEEEEAA<EEEEEEEEEEEEEEEEEEEEEAEEEEEAEEEAEE6/EEEAEEEEEEEEEA6AAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:77\tYT:Z:UU",
-                "ERR5069949.501486\t16\tMT192765.1\t5423\t42\t146M\t*\t0\t0\tTAGGTGAGTTAGGTGATGTTAGAGAAACAATGAGTTACTTGTTTCAACATGCCAATTTAGATTCTTGCAAAAGAGTCTTGAACGTGGTGTGTAAAACTTGTGGACAACAGCAGACAACCCTTAAGGGTGTAGAAGCTGTTATGTAC\tEAAAAEAEEEE6E<AEEEEEEEE<EEEEEEAAEE/EEEEEE/<EEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEEEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:146\tYT:Z:UU",
-                "ERR5069949.1331889\t0\tMT192765.1\t13010\t42\t132M\t*\t0\t0\tGTCTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAATTCAACTGTATTATCTTTCTGTGCTTTTGCTGTAGATGCTGCTAAAGCTTACAAAGATTATCTAGCTAGTGGGGGACAACCAATCACTAATTGTG\tA/AAAEEEEEEEEEEEEEEEEEEEAEEEEAEEEEEEEEEEEAEEEEEEEEEEEEEEE/EEEEE<AEAEEEEE/EAEAEEE/AEEEEEEEEEEEEEEEEEEEEAE/EEEEEEEEEEEEEEEEEEEEEEEA<EE\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:132\tYT:Z:UU",
-                "ERR5069949.2161340\t0\tMT192765.1\t17482\t42\t80M\t*\t0\t0\tAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGT\tA/AA//EEAEA/E/AEEEE6EE/EEEA/6AEEEEEEEEE6EEEAEAEE//A/EEEEEE//E/E/A//E/E/<<EE</E/E\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:80\tYT:Z:UU"
+                "ERR5069949.2151832\t16\tMT192765.1\t17453\t42\t150M\t*\t0\t0\tACGCACATTGCTAACTAAGGGCACACTAGAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGTCCTGCTGAAATTGTTGACACTGTGAGTGCTTTGGTTTATGA\tAAAA<EEEEEEAEEEAEAAAAEEEEEEEEEAAAEE<EEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:150\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.576388\t16\tMT192765.1\t5798\t42\t77M\t*\t0\t0\tGCATAGACGGTGCTTTACTTACAAAGTCCTCAGAATACAAAGGTCCTATTACGGATGTTTTCTACAAAGAAAACAGT\tEA/AEEE/<EEEEEEEEEEEAA<EEEEEEEEEEEEEEEEEEEEEAEEEEEAEEEAEE6/EEEAEEEEEEEEEA6AAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:77\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.501486\t16\tMT192765.1\t5423\t42\t146M\t*\t0\t0\tTAGGTGAGTTAGGTGATGTTAGAGAAACAATGAGTTACTTGTTTCAACATGCCAATTTAGATTCTTGCAAAAGAGTCTTGAACGTGGTGTGTAAAACTTGTGGACAACAGCAGACAACCCTTAAGGGTGTAGAAGCTGTTATGTAC\tEAAAAEAEEEE6E<AEEEEEEEE<EEEEEEAAEE/EEEEEE/<EEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEEEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAA\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:146\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.1331889\t0\tMT192765.1\t13010\t42\t132M\t*\t0\t0\tGTCTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAATTCAACTGTATTATCTTTCTGTGCTTTTGCTGTAGATGCTGCTAAAGCTTACAAAGATTATCTAGCTAGTGGGGGACAACCAATCACTAATTGTG\tA/AAAEEEEEEEEEEEEEEEEEEEAEEEEAEEEEEEEEEEEAEEEEEEEEEEEEEEE/EEEEE<AEAEEEEE/EAEAEEE/AEEEEEEEEEEEEEEEEEEEEAE/EEEEEEEEEEEEEEEEEEEEEEEA<EE\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:132\tYT:Z:UU\tRG:Z:test",
+                "ERR5069949.2161340\t0\tMT192765.1\t17482\t42\t80M\t*\t0\t0\tAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGT\tA/AA//EEAEA/E/AEEEE6EE/EEEA/6AEEEEEEEEE6EEEAEAEE//A/EEEEEE//E/E/A//E/E/<<EE</E/E\tAS:i:0\tXN:i:0\tXM:i:0\tXO:i:0\tXG:i:0\tNM:i:0\tMD:Z:80\tYT:Z:UU\tRG:Z:test"
             ],
             [
                 [
@@ -454,9 +454,9 @@
         ],
         "meta": {
             "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
+            "nextflow": "25.10.2"
         },
-        "timestamp": "2026-02-03T15:17:32.669170305"
+        "timestamp": "2026-02-25T09:58:38.116539"
     },
     "sarscov2 - [fastq1, fastq2], index, fasta, true, false - bam": {
         "content": [
diff --git a/modules/nf-core/bwamem2/mem/bwamem2-mem.diff b/modules/nf-core/bwamem2/mem/bwamem2-mem.diff
index 37262b6f..579c37ca 100644
--- a/modules/nf-core/bwamem2/mem/bwamem2-mem.diff
+++ b/modules/nf-core/bwamem2/mem/bwamem2-mem.diff
@@ -4,17 +4,19 @@ Changes in component 'nf-core/bwamem2/mem'
 Changes in 'bwamem2/mem/main.nf':
 --- modules/nf-core/bwamem2/mem/main.nf
 +++ modules/nf-core/bwamem2/mem/main.nf
-@@ -8,9 +8,7 @@
-         'community.wave.seqera.io/library/bwa-mem2_htslib_samtools:db98f81f55b64113' }"
+@@ -8,10 +8,8 @@
+         : 'community.wave.seqera.io/library/bwa-mem2_htslib_samtools:db98f81f55b64113'}"
  
      input:
 -    tuple val(meta), path(reads)
 -    tuple val(meta2), path(index)
 -    tuple val(meta3), path(fasta)
+-    val sort_bam
 +    tuple val(meta), path(reads), path(index), path(fasta)
-     val   sort_bam
++    val   sort_bam
  
      output:
+     tuple val(meta), path("*.sam"), emit: sam, optional: true
 
 'modules/nf-core/bwamem2/mem/tests/main.nf.test.snap' is unchanged
 'modules/nf-core/bwamem2/mem/tests/main.nf.test' is unchanged
diff --git a/modules/nf-core/bwamem2/mem/main.nf b/modules/nf-core/bwamem2/mem/main.nf
index 5ac348f2..e8846da3 100644
--- a/modules/nf-core/bwamem2/mem/main.nf
+++ b/modules/nf-core/bwamem2/mem/main.nf
@@ -1,23 +1,24 @@
 process BWAMEM2_MEM {
-    tag "$meta.id"
+    tag "${meta.id}"
     label 'process_high'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e0/e05ce34b46ad42810eb29f74e4e304c0cb592b2ca15572929ed8bbaee58faf01/data' :
-        'community.wave.seqera.io/library/bwa-mem2_htslib_samtools:db98f81f55b64113' }"
+    container "${workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e0/e05ce34b46ad42810eb29f74e4e304c0cb592b2ca15572929ed8bbaee58faf01/data'
+        : 'community.wave.seqera.io/library/bwa-mem2_htslib_samtools:db98f81f55b64113'}"
 
     input:
     tuple val(meta), path(reads), path(index), path(fasta)
     val   sort_bam
 
     output:
-    tuple val(meta), path("*.sam")  , emit: sam , optional:true
-    tuple val(meta), path("*.bam")  , emit: bam , optional:true
-    tuple val(meta), path("*.cram") , emit: cram, optional:true
-    tuple val(meta), path("*.crai") , emit: crai, optional:true
-    tuple val(meta), path("*.csi")  , emit: csi , optional:true
+    tuple val(meta), path("*.sam"), emit: sam, optional: true
+    tuple val(meta), path("*.bam"), emit: bam, optional: true
+    tuple val(meta), path("*.cram"), emit: cram, optional: true
+    tuple val(meta), path("*.crai"), emit: crai, optional: true
+    tuple val(meta), path("*.csi"), emit: csi, optional: true
     tuple val("${task.process}"), val('bwamem2'), eval('bwa-mem2 version | grep -o -E "[0-9]+(\\.[0-9]+)+"'), emit: versions_bwamem2, topic: versions
+    tuple val("${task.process}"), val('samtools'), eval("samtools version | sed '1!d;s/.* //'"), emit: versions_samtools, topic: versions
 
     when:
     task.ext.when == null || task.ext.when
@@ -29,39 +30,41 @@ process BWAMEM2_MEM {
     def samtools_command = sort_bam ? 'sort' : 'view'
 
     def extension_pattern = /(--output-fmt|-O)+\s+(\S+)/
-    def extension_matcher =  (args2 =~ extension_pattern)
+    def extension_matcher = (args2 =~ extension_pattern)
     def extension = extension_matcher.getCount() > 0 ? extension_matcher[0][2].toLowerCase() : "bam"
-    def reference = fasta && extension=="cram"  ? "--reference ${fasta}" : ""
-    if (!fasta && extension=="cram") error "Fasta reference is required for CRAM output"
-
+    def reference = fasta && extension == "cram" ? "--reference ${fasta}" : ""
+    if (!fasta && extension == "cram") {
+        error("Fasta reference is required for CRAM output")
+    }
     """
     INDEX=`find -L ./ -name "*.amb" | sed 's/\\.amb\$//'`
 
     bwa-mem2 \\
         mem \\
-        $args \\
-        -t $task.cpus \\
+        ${args} \\
+        -t ${task.cpus} \\
         \$INDEX \\
-        $reads \\
-        | samtools $samtools_command $args2 -@ $task.cpus ${reference} -o ${prefix}.${extension} -
+        ${reads} \\
+        | samtools ${samtools_command} ${args2} -@ ${task.cpus} ${reference} -o ${prefix}.${extension} -
     """
 
     stub:
-
     def args2 = task.ext.args2 ?: ''
     def prefix = task.ext.prefix ?: "${meta.id}"
     def extension_pattern = /(--output-fmt|-O)+\s+(\S+)/
-    def extension_matcher =  (args2 =~ extension_pattern)
+    def extension_matcher = (args2 =~ extension_pattern)
     def extension = extension_matcher.getCount() > 0 ? extension_matcher[0][2].toLowerCase() : "bam"
-    if (!fasta && extension=="cram") error "Fasta reference is required for CRAM output"
+    if (!fasta && extension == "cram") {
+        error("Fasta reference is required for CRAM output")
+    }
 
     def create_index = ""
     if (extension == "cram") {
         create_index = "touch ${prefix}.crai"
-    } else if (extension == "bam") {
+    }
+    else if (extension == "bam") {
         create_index = "touch ${prefix}.csi"
     }
-
     """
     touch ${prefix}.${extension}
     ${create_index}
diff --git a/modules/nf-core/bwamem2/mem/meta.yml b/modules/nf-core/bwamem2/mem/meta.yml
index bcfd006d..a60d6788 100644
--- a/modules/nf-core/bwamem2/mem/meta.yml
+++ b/modules/nf-core/bwamem2/mem/meta.yml
@@ -129,6 +129,16 @@ output:
       - bwa-mem2 version | grep -o -E "[0-9]+(\.[0-9]+)+":
           type: eval
           description: The expression to obtain the version of the tool
+  versions_samtools:
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - samtools version | sed '1!d;s/.* //':
+          type: eval
+          description: The expression to obtain the version of the tool
 topics:
   versions:
     - - ${task.process}:
@@ -140,6 +150,15 @@ topics:
       - bwa-mem2 version | grep -o -E "[0-9]+(\.[0-9]+)+":
           type: eval
           description: The expression to obtain the version of the tool
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - samtools version | sed '1!d;s/.* //':
+          type: eval
+          description: The expression to obtain the version of the tool
 authors:
   - "@maxulysse"
   - "@matthdsm"
diff --git a/modules/nf-core/bwamem2/mem/tests/main.nf.test b/modules/nf-core/bwamem2/mem/tests/main.nf.test
index 20e37254..be33a3a7 100644
--- a/modules/nf-core/bwamem2/mem/tests/main.nf.test
+++ b/modules/nf-core/bwamem2/mem/tests/main.nf.test
@@ -50,7 +50,6 @@ nextflow_process {
                 ).match() }
             )
         }
-
     }
 
     test("sarscov2 - fastq, index, fasta, true") {
@@ -79,7 +78,6 @@ nextflow_process {
                 ).match() }
             )
         }
-
     }
 
     test("sarscov2 - [fastq1, fastq2], index, fasta, false") {
@@ -111,7 +109,6 @@ nextflow_process {
                 ).match() }
             )
         }
-
     }
 
     test("sarscov2 - [fastq1, fastq2], index, fasta, true") {
@@ -143,7 +140,6 @@ nextflow_process {
                 ).match() }
             )
         }
-
     }
 
     test("sarscov2 - [fastq1, fastq2], index, fasta, true - stub") {
@@ -170,10 +166,8 @@ nextflow_process {
         then {
             assertAll(
                 { assert process.success },
-                { assert snapshot(process.out).match() }
+                { assert snapshot(sanitizeOutput(process.out)).match() }
             )
         }
-
     }
-
 }
diff --git a/modules/nf-core/bwamem2/mem/tests/main.nf.test.snap b/modules/nf-core/bwamem2/mem/tests/main.nf.test.snap
index 74763935..06d854b0 100644
--- a/modules/nf-core/bwamem2/mem/tests/main.nf.test.snap
+++ b/modules/nf-core/bwamem2/mem/tests/main.nf.test.snap
@@ -10,52 +10,25 @@
                         "bwamem2",
                         "2.2.1"
                     ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWAMEM2_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
                 ]
             }
         ],
+        "timestamp": "2026-02-19T11:51:27.481278728",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-09T16:25:00.500092"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "sarscov2 - [fastq1, fastq2], index, fasta, true - stub": {
         "content": [
             {
-                "0": [
-                    
-                ],
-                "1": [
-                    [
-                        {
-                            "id": "test",
-                            "single_end": false
-                        },
-                        "test.bam:md5,d41d8cd98f00b204e9800998ecf8427e"
-                    ]
-                ],
-                "2": [
-                    
-                ],
-                "3": [
-                    
-                ],
-                "4": [
-                    [
-                        {
-                            "id": "test",
-                            "single_end": false
-                        },
-                        "test.csi:md5,d41d8cd98f00b204e9800998ecf8427e"
-                    ]
-                ],
-                "5": [
-                    [
-                        "BWAMEM2_MEM",
-                        "bwamem2",
-                        "2.2.1"
-                    ]
-                ],
                 "bam": [
                     [
                         {
@@ -89,14 +62,21 @@
                         "bwamem2",
                         "2.2.1"
                     ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWAMEM2_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
                 ]
             }
         ],
+        "timestamp": "2026-02-19T11:54:06.902806102",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-09T16:25:22.004027"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "sarscov2 - [fastq1, fastq2], index, fasta, true": {
         "content": [
@@ -109,14 +89,21 @@
                         "bwamem2",
                         "2.2.1"
                     ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWAMEM2_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
                 ]
             }
         ],
+        "timestamp": "2026-02-19T11:51:40.483217643",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-09T16:25:14.131056"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "sarscov2 - fastq, index, fasta, false": {
         "content": [
@@ -129,14 +116,21 @@
                         "bwamem2",
                         "2.2.1"
                     ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWAMEM2_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
                 ]
             }
         ],
+        "timestamp": "2026-02-19T11:51:02.459481643",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-09T16:24:34.624533"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "sarscov2 - fastq, index, fasta, true": {
         "content": [
@@ -149,13 +143,20 @@
                         "bwamem2",
                         "2.2.1"
                     ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWAMEM2_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
                 ]
             }
         ],
+        "timestamp": "2026-02-19T11:51:15.170720681",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-09T16:24:47.191245"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/multiqc/main.nf b/modules/nf-core/multiqc/main.nf
index 25c2a503..5376aea1 100644
--- a/modules/nf-core/multiqc/main.nf
+++ b/modules/nf-core/multiqc/main.nf
@@ -1,26 +1,21 @@
 process MULTIQC {
-    tag "$meta.id"
+    tag "${meta.id}"
     label 'process_single'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/34/34e733a9ae16a27e80fe00f863ea1479c96416017f24a907996126283e7ecd4d/data' :
-        'community.wave.seqera.io/library/multiqc:1.33--ee7739d47738383b' }"
+    container "${workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/34/34e733a9ae16a27e80fe00f863ea1479c96416017f24a907996126283e7ecd4d/data'
+        : 'community.wave.seqera.io/library/multiqc:1.33--ee7739d47738383b'}"
 
     input:
-    tuple val(meta), path(multiqc_files, stageAs: "?/*")
-    path(multiqc_config)
-    path(extra_multiqc_config)
-    path(multiqc_logo)
-    path(replace_names)
-    path(sample_names)
+    tuple val(meta), path(multiqc_files, stageAs: "?/*"), path(multiqc_config, stageAs: "?/*"), path(multiqc_logo), path(replace_names), path(sample_names)
 
     output:
-    path "*.html"      , emit: report
-    path "*_data"      , emit: data
-    path "*_plots"     , optional:true, emit: plots
-    tuple val("${task.process}"), val('multiqc'), eval('multiqc --version | sed "s/.* //g"'), emit: versions
+    tuple val(meta), path("*.html"), emit: report
+    tuple val(meta), path("*_data"), emit: data
+    tuple val(meta), path("*_plots"), emit: plots, optional: true
     // MultiQC should not push its versions to the `versions` topic. Its input depends on the versions topic to be resolved thus outputting to the topic will let the pipeline hang forever
+    tuple val("${task.process}"), val('multiqc'), eval('multiqc --version | sed "s/.* //g"'), emit: versions
 
     when:
     task.ext.when == null || task.ext.when
@@ -28,8 +23,7 @@ process MULTIQC {
     script:
     def args = task.ext.args ?: ''
     def prefix = task.ext.prefix ? "--filename ${task.ext.prefix}.html" : ''
-    def config = multiqc_config ? "--config ${multiqc_config}" : ''
-    def extra_config = extra_multiqc_config ? "--config ${extra_multiqc_config}" : ''
+    def config = multiqc_config ? multiqc_config instanceof List ? "--config ${multiqc_config.join(' --config ')}" : "--config ${multiqc_config}" : ""
     def logo = multiqc_logo ? "--cl-config 'custom_logo: \"${multiqc_logo}\"'" : ''
     def replace = replace_names ? "--replace-names ${replace_names}" : ''
     def samples = sample_names ? "--sample-names ${sample_names}" : ''
@@ -39,7 +33,6 @@ process MULTIQC {
         ${args} \\
         ${config} \\
         ${prefix} \\
-        ${extra_config} \\
         ${logo} \\
         ${replace} \\
         ${samples} \\
@@ -51,6 +44,7 @@ process MULTIQC {
     mkdir multiqc_data
     touch multiqc_data/.stub
     mkdir multiqc_plots
+    touch multiqc_plots/.stub
     touch multiqc_report.html
     """
 }
diff --git a/modules/nf-core/multiqc/meta.yml b/modules/nf-core/multiqc/meta.yml
index 9fd34f37..ef434a9a 100644
--- a/modules/nf-core/multiqc/meta.yml
+++ b/modules/nf-core/multiqc/meta.yml
@@ -1,6 +1,6 @@
 name: multiqc
-description: Aggregate results from bioinformatics analyses across many samples into
-  a single report
+description: Aggregate results from bioinformatics analyses across many samples
+  into a single report
 keywords:
   - QC
   - bioinformatics tools
@@ -12,67 +12,81 @@ tools:
         It's a general use tool, perfect for summarising the output from numerous bioinformatics tools.
       homepage: https://multiqc.info/
       documentation: https://multiqc.info/docs/
-      licence: ["GPL-3.0-or-later"]
+      licence:
+        - "GPL-3.0-or-later"
       identifier: biotools:multiqc
 input:
-  - multiqc_files:
-      type: file
-      description: |
-        List of reports / files recognised by MultiQC, for example the html and zip output of FastQC
-      ontologies: []
-  - multiqc_config:
-      type: file
-      description: Optional config yml for MultiQC
-      pattern: "*.{yml,yaml}"
-      ontologies:
-        - edam: http://edamontology.org/format_3750 # YAML
-  - extra_multiqc_config:
-      type: file
-      description: Second optional config yml for MultiQC. Will override common sections
-        in multiqc_config.
-      pattern: "*.{yml,yaml}"
-      ontologies:
-        - edam: http://edamontology.org/format_3750 # YAML
-  - multiqc_logo:
-      type: file
-      description: Optional logo file for MultiQC
-      pattern: "*.{png}"
-      ontologies: []
-  - replace_names:
-      type: file
-      description: |
-        Optional two-column sample renaming file. First column a set of
-        patterns, second column a set of corresponding replacements. Passed via
-        MultiQC's `--replace-names` option.
-      pattern: "*.{tsv}"
-      ontologies:
-        - edam: http://edamontology.org/format_3475 # TSV
-  - sample_names:
-      type: file
-      description: |
-        Optional TSV file with headers, passed to the MultiQC --sample_names
-        argument.
-      pattern: "*.{tsv}"
-      ontologies:
-        - edam: http://edamontology.org/format_3475 # TSV
-output:
-  report:
-    - "*.html":
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'sample1', single_end:false ]
+    - multiqc_files:
         type: file
-        description: MultiQC report file
-        pattern: ".html"
+        description: |
+          List of reports / files recognised by MultiQC, for example the html and zip output of FastQC
         ontologies: []
-  data:
-    - "*_data":
-        type: directory
-        description: MultiQC data dir
-        pattern: "multiqc_data"
-  plots:
-    - "*_plots":
+    - multiqc_config:
         type: file
-        description: Plots created by MultiQC
-        pattern: "*_plots"
+        description: Optional config yml for MultiQC
+        pattern: "*.{yml,yaml}"
+        ontologies:
+          - edam: http://edamontology.org/format_3750
+    - multiqc_logo:
+        type: file
+        description: Optional logo file for MultiQC
+        pattern: "*.{png}"
         ontologies: []
+    - replace_names:
+        type: file
+        description: |
+          Optional two-column sample renaming file. First column a set of
+          patterns, second column a set of corresponding replacements. Passed via
+          MultiQC's `--replace-names` option.
+        pattern: "*.{tsv}"
+        ontologies:
+          - edam: http://edamontology.org/format_3475
+    - sample_names:
+        type: file
+        description: |
+          Optional TSV file with headers, passed to the MultiQC --sample_names
+          argument.
+        pattern: "*.{tsv}"
+        ontologies:
+          - edam: http://edamontology.org/format_3475
+output:
+  report:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*.html":
+          type: file
+          description: MultiQC report file
+          pattern: ".html"
+          ontologies: []
+  data:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*_data":
+          type: directory
+          description: MultiQC data dir
+          pattern: "multiqc_data"
+  plots:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*_plots":
+          type: file
+          description: Plots created by MultiQC
+          pattern: "*_plots"
+          ontologies: []
   versions:
     - - ${task.process}:
           type: string
diff --git a/modules/nf-core/multiqc/multiqc.diff b/modules/nf-core/multiqc/multiqc.diff
deleted file mode 100644
index f0991afe..00000000
--- a/modules/nf-core/multiqc/multiqc.diff
+++ /dev/null
@@ -1,27 +0,0 @@
-Changes in component 'nf-core/multiqc'
-'modules/nf-core/multiqc/environment.yml' is unchanged
-'modules/nf-core/multiqc/meta.yml' is unchanged
-Changes in 'multiqc/main.nf':
---- modules/nf-core/multiqc/main.nf
-+++ modules/nf-core/multiqc/main.nf
-@@ -1,4 +1,5 @@
- process MULTIQC {
-+    tag "$meta.id"
-     label 'process_single'
- 
-     conda "${moduleDir}/environment.yml"
-@@ -7,7 +8,7 @@
-         'community.wave.seqera.io/library/multiqc:1.33--ee7739d47738383b' }"
- 
-     input:
--    path multiqc_files, stageAs: "?/*"
-+    tuple val(meta), path(multiqc_files, stageAs: "?/*")
-     path(multiqc_config)
-     path(extra_multiqc_config)
-     path(multiqc_logo)
-
-'modules/nf-core/multiqc/tests/main.nf.test.snap' is unchanged
-'modules/nf-core/multiqc/tests/nextflow.config' is unchanged
-'modules/nf-core/multiqc/tests/main.nf.test' is unchanged
-'modules/nf-core/multiqc/tests/custom_prefix.config' is unchanged
-************************************************************
diff --git a/modules/nf-core/multiqc/tests/main.nf.test b/modules/nf-core/multiqc/tests/main.nf.test
index d1ae8b06..0e422eaa 100644
--- a/modules/nf-core/multiqc/tests/main.nf.test
+++ b/modules/nf-core/multiqc/tests/main.nf.test
@@ -15,25 +15,28 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = []
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [],
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
+            assert process.success
             assertAll(
-                { assert process.success },
-                { assert process.out.report[0] ==~ ".*/multiqc_report.html" },
-                { assert process.out.data[0] ==~ ".*/multiqc_data" },
-                { assert snapshot(process.out.findAll { key, val -> key.startsWith("versions")}).match() }
+                { assert snapshot(
+                    file(process.out.report[0][1]).name,
+                    file(process.out.data[0][1]).name,
+                    process.out.findAll { key, val -> key.startsWith("versions")
+                }).match() }
             )
         }
-
     }
 
     test("sarscov2 single-end [fastqc] - custom prefix") {
@@ -42,24 +45,28 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = []
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [],
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
+            assert process.success
             assertAll(
-                { assert process.success },
-                { assert process.out.report[0] ==~ ".*/custom_prefix.html" },
-                { assert process.out.data[0] ==~ ".*/custom_prefix_data" }
+                { assert snapshot(
+                    file(process.out.report[0][1]).name,
+                    file(process.out.data[0][1]).name,
+                    process.out.findAll { key, val -> key.startsWith("versions")
+                }).match() }
             )
         }
-
     }
 
     test("sarscov2 single-end [fastqc] [config]") {
@@ -67,22 +74,60 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = Channel.of(file("https://github.com/nf-core/tools/raw/dev/nf_core/pipeline-template/assets/multiqc_config.yml", checkIfExists: true))
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    file("https://github.com/nf-core/tools/raw/dev/nf_core/pipeline-template/assets/multiqc_config.yml", checkIfExists: true),
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
+            assert process.success
             assertAll(
-                { assert process.success },
-                { assert process.out.report[0] ==~ ".*/multiqc_report.html" },
-                { assert process.out.data[0] ==~ ".*/multiqc_data" },
-                { assert snapshot(process.out.findAll { key, val -> key.startsWith("versions")}).match() }
+                { assert snapshot(
+                    file(process.out.report[0][1]).name,
+                    file(process.out.data[0][1]).name,
+                    file(process.out.plots[0][1]).name,
+                    process.out.findAll { key, val -> key.startsWith("versions")
+                }).match() }
+            )
+        }
+    }
+
+    test("sarscov2 single-end [fastqc] [multiple configs]") {
+
+        when {
+            process {
+                """
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [
+                        file("https://github.com/nf-core/tools/raw/dev/nf_core/pipeline-template/assets/multiqc_config.yml", checkIfExists: true),
+                        file("https://github.com/nf-core/tools/raw/dev/nf_core/pipeline-template/assets/multiqc_config.yml", checkIfExists: true)
+                    ],
+                    [],
+                    [],
+                    []
+                ])
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(
+                    file(process.out.report[0][1]).name,
+                    file(process.out.data[0][1]).name,
+                    file(process.out.plots[0][1]).name,
+                    process.out.findAll { key, val -> key.startsWith("versions")
+                }).match() }
             )
         }
     }
@@ -94,25 +139,23 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = []
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [],
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
+            assert process.success
             assertAll(
-                { assert process.success },
-                { assert snapshot(process.out.report.collect { file(it).getName() } +
-                                process.out.data.collect { file(it).getName() } +
-                                process.out.plots.collect { file(it).getName() } +
-                                process.out.findAll { key, val -> key.startsWith("versions")} ).match() }
+                { assert snapshot(sanitizeOutput(process.out)).match() }
             )
         }
-
     }
 }
diff --git a/modules/nf-core/multiqc/tests/main.nf.test.snap b/modules/nf-core/multiqc/tests/main.nf.test.snap
index d72d35b7..c022701f 100644
--- a/modules/nf-core/multiqc/tests/main.nf.test.snap
+++ b/modules/nf-core/multiqc/tests/main.nf.test.snap
@@ -1,6 +1,9 @@
 {
-    "sarscov2 single-end [fastqc]": {
+    "sarscov2 single-end [fastqc] [multiple configs]": {
         "content": [
+            "multiqc_report.html",
+            "multiqc_data",
+            "multiqc_plots",
             {
                 "versions": [
                     [
@@ -13,35 +16,81 @@
         ],
         "meta": {
             "nf-test": "0.9.3",
-            "nextflow": "25.10.2"
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-02-26T20:21:35.851707"
+    },
+    "sarscov2 single-end [fastqc]": {
+        "content": [
+            "multiqc_report.html",
+            "multiqc_data",
+            {
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.33"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
         },
-        "timestamp": "2025-12-09T10:10:43.020315838"
+        "timestamp": "2026-02-26T15:10:36.019680076"
     },
     "sarscov2 single-end [fastqc] - stub": {
         "content": [
-            [
-                "multiqc_report.html",
-                "multiqc_data",
-                "multiqc_plots",
-                {
-                    "versions": [
+            {
+                "data": [
+                    [
+                        {
+                            "id": "FASTQC"
+                        },
                         [
-                            "MULTIQC",
-                            "multiqc",
-                            "1.33"
+                            ".stub:md5,d41d8cd98f00b204e9800998ecf8427e"
                         ]
                     ]
-                }
-            ]
+                ],
+                "plots": [
+                    [
+                        {
+                            "id": "FASTQC"
+                        },
+                        [
+                            ".stub:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "report": [
+                    [
+                        {
+                            "id": "FASTQC"
+                        },
+                        "multiqc_report.html:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.33"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.2"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
         },
-        "timestamp": "2025-12-09T10:11:14.131950776"
+        "timestamp": "2026-02-26T15:14:39.789193051"
     },
     "sarscov2 single-end [fastqc] [config]": {
         "content": [
+            "multiqc_report.html",
+            "multiqc_data",
+            "multiqc_plots",
             {
                 "versions": [
                     [
@@ -53,9 +102,29 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.2"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-02-26T15:21:29.116129274"
+    },
+    "sarscov2 single-end [fastqc] - custom prefix": {
+        "content": [
+            "custom_prefix.html",
+            "custom_prefix_data",
+            {
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.33"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
         },
-        "timestamp": "2025-12-09T10:11:07.15692209"
+        "timestamp": "2026-02-26T15:10:43.419877592"
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/multiqcsav/environment.yml b/modules/nf-core/multiqcsav/environment.yml
new file mode 100644
index 00000000..092076ff
--- /dev/null
+++ b/modules/nf-core/multiqcsav/environment.yml
@@ -0,0 +1,13 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  # renovate: datasource=conda depName=bioconda/multiqc
+  - bioconda::multiqc=1.33
+  # renovate: datasource=conda depName=bioconda/multiqc_sav
+  - bioconda::multiqc_sav=0.2.0
+  - pip=25.3
+  - pip:
+      - interop==1.9.0
diff --git a/modules/nf-core/multiqcsav/main.nf b/modules/nf-core/multiqcsav/main.nf
new file mode 100644
index 00000000..833ad6df
--- /dev/null
+++ b/modules/nf-core/multiqcsav/main.nf
@@ -0,0 +1,53 @@
+process MULTIQCSAV {
+    tag "${meta.id}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+    container "${workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/36/3634362a0bf5a0530a6459bdba392622262d6de6cc0062e9a293bacc3098b323/data'
+        : 'community.wave.seqera.io/library/multiqc_multiqc_sav_pip_interop:b142653b3920c82b'}"
+
+    input:
+    tuple  val(meta), path(xml), path(interop_bin, stageAs: "InterOp/*"), path(extra_multiqc_files, stageAs: "?/*"), path(multiqc_config, stageAs: "?/*"), path(multiqc_logo), path(replace_names), path(sample_names)
+
+    output:
+    tuple val(meta), path("*.html"), emit: report
+    tuple val(meta), path("*_data"), emit: data
+    tuple val(meta), path("*_plots"), emit: plots, optional: true
+    // MultiQC should not push its versions to the `versions` topic. Its input depends on the versions topic to be resolved thus outputting to the topic will let the pipeline hang forever
+    tuple val("${task.process}"), val('multiqc'), eval('multiqc --version | sed "s/.* //g"'), emit: versions
+    tuple val("${task.process}"), val('multiqcsav'), eval('python -c "import multiqc_sav; print(multiqc_sav.__version__)"'), emit: versions_multiqcsav
+    tuple val("${task.process}"), val('interop'), eval('python -c "import interop; print(interop.__version__)"'), emit: versions_interop
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args = task.ext.args ?: ''
+    def prefix = task.ext.prefix ? "--filename ${task.ext.prefix}.html" : ''
+    def config = multiqc_config ? multiqc_config instanceof List ? "--config ${multiqc_config.join(' --config ')}" : "--config ${multiqc_config}" : ""
+    def logo = multiqc_logo ? "--cl-config 'custom_logo: \"${multiqc_logo}\"'" : ''
+    def replace = replace_names ? "--replace-names ${replace_names}" : ''
+    def samples = sample_names ? "--sample-names ${sample_names}" : ''
+    """
+    export TMPDIR="\$PWD/tmp"
+    multiqc \\
+        --force \\
+        ${args} \\
+        ${config} \\
+        ${prefix} \\
+        ${logo} \\
+        ${replace} \\
+        ${samples} \\
+        .
+    """
+
+    stub:
+    """
+    mkdir multiqc_data
+    touch multiqc_data/.stub
+    mkdir multiqc_plots
+    touch multiqc_plots/.stub
+    touch multiqc_report.html
+    """
+}
diff --git a/modules/nf-core/multiqcsav/meta.yml b/modules/nf-core/multiqcsav/meta.yml
new file mode 100644
index 00000000..86545841
--- /dev/null
+++ b/modules/nf-core/multiqcsav/meta.yml
@@ -0,0 +1,139 @@
+name: multiqcsav
+description: Aggregate results from bioinformatics analyses across many samples
+  into a single report, with support for multiqc_sav plugin
+keywords:
+  - QC
+  - bioinformatics tools
+  - Beautiful stand-alone HTML report
+  - Illumina
+  - Sequencing Analysis Viewer
+  - SAV
+tools:
+  - multiqc:
+      description: |
+        MultiQC searches a given directory for analysis logs and compiles a HTML report.
+        It's a general use tool, perfect for summarising the output from numerous bioinformatics tools.
+      homepage: https://multiqc.info/
+      documentation: https://multiqc.info/docs/
+      licence:
+        - "GPL-3.0-or-later"
+      identifier: biotools:multiqc
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'sample1', single_end:false ]
+    - xml:
+        type: file
+        description: xml files from an Illumina sequencing run
+        pattern: "*.xml"
+        ontologies:
+          - edam: http://edamontology.org/format_2332
+    - interop_bin:
+        type: file
+        description: Illumina InterOp binary files
+        pattern: "InterOp/*.bin"
+        ontologies:
+          - edam: http://edamontology.org/format_2333
+    - extra_multiqc_files:
+        type: file
+        description: |
+          List of reports / files recognised by MultiQC, for example the html and zip output of FastQC
+        ontologies: []
+    - multiqc_config:
+        type: file
+        description: Optional config yml for MultiQC
+        pattern: "*.{yml,yaml}"
+        ontologies:
+          - edam: http://edamontology.org/format_3750
+    - multiqc_logo:
+        type: file
+        description: Optional logo file for MultiQC
+        pattern: "*.{png}"
+        ontologies: []
+    - replace_names:
+        type: file
+        description: |
+          Optional two-column sample renaming file. First column a set of
+          patterns, second column a set of corresponding replacements. Passed via
+          MultiQC's `--replace-names` option.
+        pattern: "*.{tsv}"
+        ontologies:
+          - edam: http://edamontology.org/format_3475
+    - sample_names:
+        type: file
+        description: |
+          Optional TSV file with headers, passed to the MultiQC --sample_names
+          argument.
+        pattern: "*.{tsv}"
+        ontologies:
+          - edam: http://edamontology.org/format_3475
+output:
+  report:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*.html":
+          type: file
+          description: MultiQC report file
+          pattern: ".html"
+          ontologies: []
+  data:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*_data":
+          type: directory
+          description: MultiQC data dir
+          pattern: "multiqc_data"
+  plots:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*_plots":
+          type: file
+          description: Plots created by MultiQC
+          pattern: "*_data"
+          ontologies: []
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - multiqc:
+          type: string
+          description: The tool name
+      - multiqc --version | sed "s/.* //g":
+          type: eval
+          description: The expression to obtain the version of the tool
+  versions_interop:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - interop:
+          type: string
+          description: The tool name
+      - python -c "import interop; print(interop.__version__)":
+          type: eval
+          description: The expression to obtain the version of the tool
+  versions_multiqcsav:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - multiqcsav:
+          type: string
+          description: The name of the tool
+      - python -c "import multiqc_sav; print(multiqc_sav.__version__)":
+          type: eval
+          description: The expression to obtain the version of the tool
+authors:
+  - "@matthdsm"
+  - "@delfiterradas"
+maintainers:
+  - "@matthdsm"
diff --git a/modules/nf-core/multiqcsav/tests/main.nf.test b/modules/nf-core/multiqcsav/tests/main.nf.test
new file mode 100644
index 00000000..3f48121e
--- /dev/null
+++ b/modules/nf-core/multiqcsav/tests/main.nf.test
@@ -0,0 +1,136 @@
+nextflow_process {
+
+    name "Test Process MULTIQCSAV"
+    script "../main.nf"
+    process "MULTIQCSAV"
+
+    tag "modules"
+    tag "modules_nfcore"
+    tag "untar"
+    tag "multiqcsav"
+
+    test("NovaSeq6000") {
+        setup {
+            run("UNTAR") {
+                script "../../untar/main.nf"
+                process {
+                """
+                input[0] = [
+                    [ id: 'NovaSeq6000' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bcl/200624_A00834_0183_BHMTFYDRXX.tar.gz', checkIfExists: true)
+                ]
+                """
+                }
+            }
+        }
+
+        when {
+            process {
+                """
+                ch_sav_in = UNTAR.out.untar.map{ meta, untar ->
+
+                    def xml = []
+                    untar.eachFileRecurse { file ->
+                        if (file.fileName.toString() == 'RunInfo.xml') {
+                            xml << file
+                        }
+                    }
+
+                    def interop = []
+                    untar.eachFileRecurse { file ->
+                        if (file.parent.name == 'InterOp' && file.fileName.toString().endsWith(".bin")) {
+                            interop << file
+                        }
+                    }
+
+                    return [
+                        meta,
+                        xml,
+                        interop,
+                        [],
+                        [],
+                        [],
+                        [],
+                        []
+                    ]
+                }
+
+                input[0] = ch_sav_in
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(
+                    file(process.out.report[0][1]).name,
+                    file(process.out.data[0][1]).name,
+                    process.out.findAll { key, val -> key.startsWith("versions")
+                }).match() }
+            )
+        }
+    }
+
+    test("NovaSeq6000 - stub") {
+
+        options "-stub"
+
+        setup {
+            run("UNTAR") {
+                script "../../untar/main.nf"
+                process {
+                """
+                input[0] = [
+                    [ id: 'NovaSeq6000' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bcl/200624_A00834_0183_BHMTFYDRXX.tar.gz', checkIfExists: true)
+                ]
+                """
+                }
+            }
+        }
+
+        when {
+            process {
+                """
+                ch_sav_in = UNTAR.out.untar.map{ meta, untar ->
+
+                    def xml = []
+                    untar.eachFileRecurse { file ->
+                        if (file.fileName.toString() == 'RunInfo.xml') {
+                            xml << file
+                        }
+                    }
+
+                    def interop = []
+                    untar.eachFileRecurse { file ->
+                        if (file.parent.name == 'InterOp' && file.fileName.toString().endsWith(".bin")) {
+                            interop << file
+                        }
+                    }
+
+                    return [
+                        meta,
+                        xml,
+                        interop,
+                        [],
+                        [],
+                        [],
+                        [],
+                        []
+                    ]
+                }
+
+                input[0] = ch_sav_in
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(sanitizeOutput(process.out)).match() }
+            )
+        }
+    }
+}
diff --git a/modules/nf-core/multiqcsav/tests/main.nf.test.snap b/modules/nf-core/multiqcsav/tests/main.nf.test.snap
new file mode 100644
index 00000000..3f3783a1
--- /dev/null
+++ b/modules/nf-core/multiqcsav/tests/main.nf.test.snap
@@ -0,0 +1,96 @@
+{
+    "NovaSeq6000": {
+        "content": [
+            "multiqc_report.html",
+            "multiqc_data",
+            {
+                "versions": [
+                    [
+                        "MULTIQCSAV",
+                        "multiqc",
+                        "1.33"
+                    ]
+                ],
+                "versions_interop": [
+                    [
+                        "MULTIQCSAV",
+                        "interop",
+                        "1.9.0"
+                    ]
+                ],
+                "versions_multiqcsav": [
+                    [
+                        "MULTIQCSAV",
+                        "multiqcsav",
+                        "0.2.0"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-02-26T15:17:26.065765698"
+    },
+    "NovaSeq6000 - stub": {
+        "content": [
+            {
+                "data": [
+                    [
+                        {
+                            "id": "NovaSeq6000"
+                        },
+                        [
+                            ".stub:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "plots": [
+                    [
+                        {
+                            "id": "NovaSeq6000"
+                        },
+                        [
+                            ".stub:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "report": [
+                    [
+                        {
+                            "id": "NovaSeq6000"
+                        },
+                        "multiqc_report.html:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "versions": [
+                    [
+                        "MULTIQCSAV",
+                        "multiqc",
+                        "1.33"
+                    ]
+                ],
+                "versions_interop": [
+                    [
+                        "MULTIQCSAV",
+                        "interop",
+                        "1.9.0"
+                    ]
+                ],
+                "versions_multiqcsav": [
+                    [
+                        "MULTIQCSAV",
+                        "multiqcsav",
+                        "0.2.0"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-02-26T15:18:42.648653899"
+    }
+}
\ No newline at end of file
diff --git a/modules/nf-core/picard/collecthsmetrics/main.nf b/modules/nf-core/picard/collecthsmetrics/main.nf
index 22d95d57..2b9b7588 100644
--- a/modules/nf-core/picard/collecthsmetrics/main.nf
+++ b/modules/nf-core/picard/collecthsmetrics/main.nf
@@ -1,18 +1,18 @@
 process PICARD_COLLECTHSMETRICS {
-    tag "$meta.id"
+    tag "${meta.id}"
     label 'process_single'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/08/0861295baa7c01fc593a9da94e82b44a729dcaf8da92be8e565da109aa549b25/data' :
-        'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6' }"
+    container "${workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/08/0861295baa7c01fc593a9da94e82b44a729dcaf8da92be8e565da109aa549b25/data'
+        : 'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6'}"
 
     input:
     tuple val(meta), path(bam), path(bai), path(bait_intervals, stageAs: "bait/*"), path(target_intervals, stageAs: "target/*") ,path(fasta) ,path(fai) ,path(dict)
 
     output:
-    tuple val(meta), path("*_metrics")  , emit: metrics
-    tuple val("${task.process}"), val('picard'), eval("picard CollectHsMetrics --version 2>&1 | sed -n 's/^Version:*//p'"), topic: versions, emit: versions_picard
+    tuple val(meta), path("*_metrics"), emit: metrics
+    tuple val("${task.process}"), val('picard'), eval("picard CollectHsMetrics --version 2>&1 | sed -n 's/.*Version://p'"), topic: versions, emit: versions_picard
 
     when:
     task.ext.when == null || task.ext.when
@@ -24,40 +24,38 @@ process PICARD_COLLECTHSMETRICS {
 
     def avail_mem = 3072
     if (!task.memory) {
-        log.info '[Picard CollectHsMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.'
-    } else {
-        avail_mem = (task.memory.mega*0.8).intValue()
+        log.info('[Picard CollectHsMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.')
+    }
+    else {
+        avail_mem = (task.memory.mega * 0.8).intValue()
     }
 
     def bait_interval_list = bait_intervals
     def bait_intervallist_cmd = ""
-    if (bait_intervals =~ /.(bed|bed.gz)$/){
+    if (bait_intervals =~ /.(bed|bed.gz)$/) {
         bait_interval_list = bait_intervals.toString().replaceAll(/.(bed|bed.gz)$/, ".interval_list")
         bait_intervallist_cmd = "picard -Xmx${avail_mem}M BedToIntervalList --INPUT ${bait_intervals} --OUTPUT ${bait_interval_list} --SEQUENCE_DICTIONARY ${dict} --TMP_DIR ."
     }
 
     def target_interval_list = target_intervals
     def target_intervallist_cmd = ""
-    if (target_intervals =~ /.(bed|bed.gz)$/){
+    if (target_intervals =~ /.(bed|bed.gz)$/) {
         target_interval_list = target_intervals.toString().replaceAll(/.(bed|bed.gz)$/, ".interval_list")
         target_intervallist_cmd = "picard -Xmx${avail_mem}M BedToIntervalList --INPUT ${target_intervals} --OUTPUT ${target_interval_list} --SEQUENCE_DICTIONARY ${dict} --TMP_DIR ."
     }
-
-
     """
     export TMP=\$PWD
-
-    $bait_intervallist_cmd
-    $target_intervallist_cmd
+    ${bait_intervallist_cmd}
+    ${target_intervallist_cmd}
 
     picard \\
         -Xmx${avail_mem}M \\
         CollectHsMetrics \\
-        $args \\
-        $reference \\
-        --BAIT_INTERVALS $bait_interval_list \\
-        --TARGET_INTERVALS $target_interval_list \\
-        --INPUT $bam \\
+        ${args} \\
+        ${reference} \\
+        --BAIT_INTERVALS ${bait_interval_list} \\
+        --TARGET_INTERVALS ${target_interval_list} \\
+        --INPUT ${bam} \\
         --OUTPUT ${prefix}.CollectHsMetrics.coverage_metrics \\
         --TMP_DIR .
     """
diff --git a/modules/nf-core/picard/collecthsmetrics/meta.yml b/modules/nf-core/picard/collecthsmetrics/meta.yml
index a21aa6ec..89bc502c 100644
--- a/modules/nf-core/picard/collecthsmetrics/meta.yml
+++ b/modules/nf-core/picard/collecthsmetrics/meta.yml
@@ -106,7 +106,7 @@ output:
       - picard:
           type: string
           description: The tool name
-      - "picard CollectHsMetrics --version 2>&1 | sed -n 's/^Version:*//p'":
+      - "picard CollectHsMetrics --version 2>&1 | sed -n 's/.*Version://p'":
           type: string
           description: The command used to generate the version of the tool
 
@@ -118,7 +118,7 @@ topics:
       - picard:
           type: string
           description: The tool name
-      - "picard CollectHsMetrics --version 2>&1 | sed -n 's/^Version:*//p'":
+      - "picard CollectHsMetrics --version 2>&1 | sed -n 's/.*Version://p'":
           type: string
           description: The command used to generate the version of the tool
 authors:
diff --git a/modules/nf-core/picard/collecthsmetrics/picard-collecthsmetrics.diff b/modules/nf-core/picard/collecthsmetrics/picard-collecthsmetrics.diff
index f4bf1eda..f9b0281a 100644
--- a/modules/nf-core/picard/collecthsmetrics/picard-collecthsmetrics.diff
+++ b/modules/nf-core/picard/collecthsmetrics/picard-collecthsmetrics.diff
@@ -5,18 +5,18 @@ Changes in 'picard/collecthsmetrics/main.nf':
 --- modules/nf-core/picard/collecthsmetrics/main.nf
 +++ modules/nf-core/picard/collecthsmetrics/main.nf
 @@ -8,11 +8,7 @@
-         'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6' }"
+         : 'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6'}"
  
      input:
 -    tuple val(meta), path(bam), path(bai), path(bait_intervals, stageAs: "baits/*"), path(target_intervals, stageAs: 'targets/*')
 -    tuple val(meta2), path(ref)
 -    tuple val(meta3), path(ref_fai)
 -    tuple val(meta4), path(ref_dict)
--    tuple val(meta5), path(ref_gzi) // ref_gzi only required if reference is gzipped
+-    tuple val(meta5), path(ref_gzi)
 +    tuple val(meta), path(bam), path(bai), path(bait_intervals, stageAs: "bait/*"), path(target_intervals, stageAs: "target/*") ,path(fasta) ,path(fai) ,path(dict)
  
      output:
-     tuple val(meta), path("*_metrics")  , emit: metrics
+     tuple val(meta), path("*_metrics"), emit: metrics
 @@ -24,7 +20,7 @@
      script:
      def args = task.ext.args ?: ''
@@ -26,9 +26,9 @@ Changes in 'picard/collecthsmetrics/main.nf':
  
      def avail_mem = 3072
      if (!task.memory) {
-@@ -37,18 +33,19 @@
+@@ -38,16 +34,17 @@
      def bait_intervallist_cmd = ""
-     if (bait_intervals =~ /.(bed|bed.gz)$/){
+     if (bait_intervals =~ /.(bed|bed.gz)$/) {
          bait_interval_list = bait_intervals.toString().replaceAll(/.(bed|bed.gz)$/, ".interval_list")
 -        bait_intervallist_cmd = "picard -Xmx${avail_mem}M BedToIntervalList --INPUT ${bait_intervals} --OUTPUT ${bait_interval_list} --SEQUENCE_DICTIONARY ${ref_dict} --TMP_DIR ."
 +        bait_intervallist_cmd = "picard -Xmx${avail_mem}M BedToIntervalList --INPUT ${bait_intervals} --OUTPUT ${bait_interval_list} --SEQUENCE_DICTIONARY ${dict} --TMP_DIR ."
@@ -36,24 +36,21 @@ Changes in 'picard/collecthsmetrics/main.nf':
  
      def target_interval_list = target_intervals
      def target_intervallist_cmd = ""
-     if (target_intervals =~ /.(bed|bed.gz)$/){
+     if (target_intervals =~ /.(bed|bed.gz)$/) {
          target_interval_list = target_intervals.toString().replaceAll(/.(bed|bed.gz)$/, ".interval_list")
 -        target_intervallist_cmd = "picard -Xmx${avail_mem}M BedToIntervalList --INPUT ${target_intervals} --OUTPUT ${target_interval_list} --SEQUENCE_DICTIONARY ${ref_dict} --TMP_DIR ."
 +        target_intervallist_cmd = "picard -Xmx${avail_mem}M BedToIntervalList --INPUT ${target_intervals} --OUTPUT ${target_interval_list} --SEQUENCE_DICTIONARY ${dict} --TMP_DIR ."
      }
- 
- 
      """
 +    export TMP=\$PWD
+     ${bait_intervallist_cmd}
+     ${target_intervallist_cmd}
  
-     $bait_intervallist_cmd
-     $target_intervallist_cmd
-@@ -61,8 +58,8 @@
-         --BAIT_INTERVALS $bait_interval_list \\
-         --TARGET_INTERVALS $target_interval_list \\
-         --INPUT $bam \\
+@@ -59,7 +56,8 @@
+         --BAIT_INTERVALS ${bait_interval_list} \\
+         --TARGET_INTERVALS ${target_interval_list} \\
+         --INPUT ${bam} \\
 -        --OUTPUT ${prefix}.CollectHsMetrics.coverage_metrics
--
 +        --OUTPUT ${prefix}.CollectHsMetrics.coverage_metrics \\
 +        --TMP_DIR .
      """
diff --git a/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test b/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test
index 07a9ccf4..d7366111 100644
--- a/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test
+++ b/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test
@@ -40,11 +40,9 @@ nextflow_process {
                     size,
                     lines,
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
-
     }
 
     test("sarscov2 - bam - gzippedfa") {
@@ -78,17 +76,13 @@ nextflow_process {
                     size,
                     lines,
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
-
     }
 
     test("sarscov2 - bam - stub") {
-
         options "-stub"
-
         when {
             process {
                 """
@@ -110,10 +104,9 @@ nextflow_process {
         then {
             assertAll(
                 { assert process.success },
-                { assert snapshot(process.out).match() }
+                { assert snapshot(sanitizeOutput(process.out)).match() }
             )
         }
-
     }
 
     test("sarscov2 - bam - nofasta") {
@@ -147,11 +140,9 @@ nextflow_process {
                     size,
                     lines,
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
-
     }
 
     test("sarscov2 - bam - bed") {
@@ -186,11 +177,9 @@ nextflow_process {
                     size,
                     lines,
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
-
     }
 
     test("sarscov2 - bam - samebed") {
@@ -225,10 +214,8 @@ nextflow_process {
                     size,
                     lines,
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
-
     }
 }
diff --git a/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test.snap b/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test.snap
index 74d9441f..43385314 100644
--- a/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test.snap
+++ b/modules/nf-core/picard/collecthsmetrics/tests/main.nf.test.snap
@@ -115,31 +115,15 @@
                 ]
             }
         ],
+        "timestamp": "2026-01-05T17:03:29.566021877",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-01-05T17:03:29.566021877"
+        }
     },
     "sarscov2 - bam - stub": {
         "content": [
             {
-                "0": [
-                    [
-                        {
-                            "id": "test",
-                            "single_end": false
-                        },
-                        "test.CollectHsMetrics.coverage_metrics:md5,d41d8cd98f00b204e9800998ecf8427e"
-                    ]
-                ],
-                "1": [
-                    [
-                        "PICARD_COLLECTHSMETRICS",
-                        "picard",
-                        "3.4.0"
-                    ]
-                ],
                 "metrics": [
                     [
                         {
@@ -158,11 +142,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-19T17:36:03.822502867",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-01-05T17:03:13.333276975"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "sarscov2 - bam - gzippedfa": {
         "content": [
@@ -280,11 +264,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-01-05T17:03:04.382110367",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-01-05T17:03:04.382110367"
+        }
     },
     "sarscov2 - bam - samebed": {
         "content": [
@@ -402,11 +386,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-01-05T17:13:22.872920293",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-01-05T17:13:22.872920293"
+        }
     },
     "sarscov2 - bam": {
         "content": [
@@ -524,11 +508,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-01-05T17:02:47.615784738",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-01-05T17:02:47.615784738"
+        }
     },
     "sarscov2 - bam - bed": {
         "content": [
@@ -646,10 +630,10 @@
                 ]
             }
         ],
+        "timestamp": "2026-01-05T17:13:02.812282052",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-01-05T17:13:02.812282052"
+        }
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/picard/collectmultiplemetrics/main.nf b/modules/nf-core/picard/collectmultiplemetrics/main.nf
index 11b00464..59842bdd 100644
--- a/modules/nf-core/picard/collectmultiplemetrics/main.nf
+++ b/modules/nf-core/picard/collectmultiplemetrics/main.nf
@@ -1,19 +1,19 @@
 process PICARD_COLLECTMULTIPLEMETRICS {
-    tag "$meta.id"
+    tag "${meta.id}"
     label 'process_single'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/08/0861295baa7c01fc593a9da94e82b44a729dcaf8da92be8e565da109aa549b25/data' :
-        'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6' }"
+    container "${workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/08/0861295baa7c01fc593a9da94e82b44a729dcaf8da92be8e565da109aa549b25/data'
+        : 'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6'}"
 
     input:
     tuple val(meta) , path(bam), path(bai), path(intervals), path(fasta) ,path(fai), path(dict)
 
     output:
     tuple val(meta), path("*_metrics"), emit: metrics
-    tuple val(meta), path("*.pdf")    , emit: pdf, optional: true
-    tuple val("${task.process}"), val('picard'), eval("picard CollectMultipleMetrics --version 2>&1 | sed -n 's/^Version:*//p'"), topic: versions, emit: versions_picard
+    tuple val(meta), path("*.pdf"), emit: pdf, optional: true
+    tuple val("${task.process}"), val('picard'), eval("picard CollectMultipleMetrics --version 2>&1 | sed -n 's/.*Version://p'"), topic: versions, emit: versions_picard
 
     when:
     task.ext.when == null || task.ext.when
@@ -25,21 +25,22 @@ process PICARD_COLLECTMULTIPLEMETRICS {
     def reference_cmd = fasta ? "--REFERENCE_SEQUENCE ${fasta}" : ""
     def avail_mem = 3072
     if (!task.memory) {
-        log.info '[Picard CollectMultipleMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.'
-    } else {
-        avail_mem = (task.memory.mega*0.8).intValue()
+        log.info('[Picard CollectMultipleMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.')
+    }
+    else {
+        avail_mem = (task.memory.mega * 0.8).intValue()
     }
     """
     export TMP=\$PWD
     picard \\
         -Xmx${avail_mem}M \\
         CollectMultipleMetrics \\
-        $args \\
-        --INPUT $bam \\
+        ${args} \\
+        --INPUT ${bam} \\
         --OUTPUT ${prefix}.CollectMultipleMetrics \\
         --TMP_DIR . \\
-        $reference_cmd \\
-        $intervals_cmd
+        ${reference_cmd} \\
+        ${intervals_cmd}
     """
 
     stub:
diff --git a/modules/nf-core/picard/collectmultiplemetrics/meta.yml b/modules/nf-core/picard/collectmultiplemetrics/meta.yml
index 1ea47244..213d600b 100644
--- a/modules/nf-core/picard/collectmultiplemetrics/meta.yml
+++ b/modules/nf-core/picard/collectmultiplemetrics/meta.yml
@@ -81,7 +81,7 @@ output:
       - picard:
           type: string
           description: The tool name
-      - "picard CollectMultipleMetrics --version 2>&1 | sed -n 's/^Version:*//p'":
+      - "picard CollectMultipleMetrics --version 2>&1 | sed -n 's/.*Version://p'":
           type: string
           description: The command used to generate the version of the tool
 
@@ -93,7 +93,7 @@ topics:
       - picard:
           type: string
           description: The tool name
-      - "picard CollectMultipleMetrics --version 2>&1 | sed -n 's/^Version:*//p'":
+      - "picard CollectMultipleMetrics --version 2>&1 | sed -n 's/.*Version://p'":
           type: string
           description: The command used to generate the version of the tool
 
diff --git a/modules/nf-core/picard/collectmultiplemetrics/picard-collectmultiplemetrics.diff b/modules/nf-core/picard/collectmultiplemetrics/picard-collectmultiplemetrics.diff
index 1d7cbf60..780c0862 100644
--- a/modules/nf-core/picard/collectmultiplemetrics/picard-collectmultiplemetrics.diff
+++ b/modules/nf-core/picard/collectmultiplemetrics/picard-collectmultiplemetrics.diff
@@ -5,10 +5,10 @@ Changes in 'picard/collectmultiplemetrics/main.nf':
 --- modules/nf-core/picard/collectmultiplemetrics/main.nf
 +++ modules/nf-core/picard/collectmultiplemetrics/main.nf
 @@ -8,9 +8,7 @@
-         'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6' }"
+         : 'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6'}"
  
      input:
--    tuple val(meta) , path(bam), path(bai)
+-    tuple val(meta), path(bam), path(bai)
 -    tuple val(meta2), path(fasta)
 -    tuple val(meta3), path(fai)
 +    tuple val(meta) , path(bam), path(bai), path(intervals), path(fasta) ,path(fai), path(dict)
@@ -24,33 +24,25 @@ Changes in 'picard/collectmultiplemetrics/main.nf':
 +    def reference_cmd = fasta ? "--REFERENCE_SEQUENCE ${fasta}" : ""
      def avail_mem = 3072
      if (!task.memory) {
-         log.info '[Picard CollectMultipleMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.'
-@@ -31,14 +30,16 @@
-         avail_mem = (task.memory.mega*0.8).intValue()
+         log.info('[Picard CollectMultipleMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.')
+@@ -32,13 +31,16 @@
+         avail_mem = (task.memory.mega * 0.8).intValue()
      }
      """
 +    export TMP=\$PWD
      picard \\
          -Xmx${avail_mem}M \\
          CollectMultipleMetrics \\
-         $args \\
-         --INPUT $bam \\
+         ${args} \\
+         --INPUT ${bam} \\
          --OUTPUT ${prefix}.CollectMultipleMetrics \\
--        $reference
--
+-        ${reference}
 +        --TMP_DIR . \\
-+        $reference_cmd \\
-+        $intervals_cmd
++        ${reference_cmd} \\
++        ${intervals_cmd}
      """
  
      stub:
-@@ -54,6 +55,5 @@
-     touch ${prefix}.CollectMultipleMetrics.quality_by_cycle.pdf
-     touch ${prefix}.CollectMultipleMetrics.insert_size_histogram.pdf
-     touch ${prefix}.CollectMultipleMetrics.quality_distribution_metrics
--
-     """
- }
 
 'modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test.snap' is unchanged
 'modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test' is unchanged
diff --git a/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test b/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test
index 08456941..0037acab 100644
--- a/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test
+++ b/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test
@@ -16,16 +16,15 @@ nextflow_process {
             process {
                 """
                 input[0] = [
-				    [ id:'test', single_end:false ], // meta map
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
-				]
-				input[1] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
-				]
-				input[2] = [[id:'genome'],[]]
-
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [[id:'genome'],[]]
                 """
             }
         }
@@ -37,8 +36,7 @@ nextflow_process {
                     process.out.metrics[0][1].collect { file(it).name }.toSorted(),
                     process.out.pdf[0][1].collect { file(it).name }.toSorted(),
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
     }
@@ -49,13 +47,12 @@ nextflow_process {
             process {
                 """
                 input[0] = [
-				    [ id:'test', single_end:false ], // meta map
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
-				]
-				input[1] = [[id:'genome'],[]]
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
+                ]
+                input[1] = [[id:'genome'],[]]
                 input[2] = [[id:'genome'],[]]
-
                 """
             }
         }
@@ -67,8 +64,7 @@ nextflow_process {
                     process.out.metrics[0][1].collect { file(it).name }.toSorted(),
                     process.out.pdf[0][1].collect { file(it).name }.toSorted(),
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
     }
@@ -79,19 +75,18 @@ nextflow_process {
             process {
                 """
                 input[0] = [
-				[ id:'test', single_end:false ], // meta map
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
-				]
-				input[1] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
-				]
-				input[2] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
-				]
-
+                [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ]
                 """
             }
         }
@@ -103,10 +98,89 @@ nextflow_process {
                     process.out.metrics[0][1].collect { file(it).name }.toSorted(),
                     process.out.pdf[0][1].collect { file(it).name }.toSorted(),
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
+            )
+        }
+    }
+
+    test("test-picard-collectmultiplemetrics - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [[id:'genome'],[]]
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(sanitizeOutput(process.out)).match() }
+            )
+        }
+    }
+
+    test("test-picard-collectmultiplemetrics-nofasta - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
+                ]
+                input[1] = [[id:'genome'],[]]
+                input[2] = [[id:'genome'],[]]
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(sanitizeOutput(process.out)).match() }
             )
         }
     }
 
+    test("test-picard-collectmultiplemetrics-cram - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = [
+                [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true)
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ]
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(sanitizeOutput(process.out)).match() }
+            )
+        }
+    }
 }
diff --git a/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test.snap b/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test.snap
index 1acf3776..393ed100 100644
--- a/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test.snap
+++ b/modules/nf-core/picard/collectmultiplemetrics/tests/main.nf.test.snap
@@ -25,11 +25,107 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-02T10:22:21.230301646",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-02-02T10:22:21.230301646"
+        }
+    },
+    "test-picard-collectmultiplemetrics - stub": {
+        "content": [
+            {
+                "metrics": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        [
+                            "test.CollectMultipleMetrics.alignment_summary_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.base_distribution_by_cycle_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.insert_size_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_by_cycle_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_distribution_metrics:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "pdf": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        [
+                            "test.CollectMultipleMetrics.base_distribution_by_cycle.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.insert_size_histogram.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_by_cycle.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_distribution.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.read_length_histogram.pdf:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "versions_picard": [
+                    [
+                        "PICARD_COLLECTMULTIPLEMETRICS",
+                        "picard",
+                        "3.4.0"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-02-20T10:32:38.701455244",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
+    },
+    "test-picard-collectmultiplemetrics-nofasta - stub": {
+        "content": [
+            {
+                "metrics": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        [
+                            "test.CollectMultipleMetrics.alignment_summary_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.base_distribution_by_cycle_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.insert_size_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_by_cycle_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_distribution_metrics:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "pdf": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        [
+                            "test.CollectMultipleMetrics.base_distribution_by_cycle.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.insert_size_histogram.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_by_cycle.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_distribution.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.read_length_histogram.pdf:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "versions_picard": [
+                    [
+                        "PICARD_COLLECTMULTIPLEMETRICS",
+                        "picard",
+                        "3.4.0"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-02-20T10:32:48.923918624",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "test-picard-collectmultiplemetrics-cram": {
         "content": [
@@ -57,11 +153,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-02T10:23:52.23446844",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-02-02T10:23:52.23446844"
+        }
     },
     "test-picard-collectmultiplemetrics-nofasta": {
         "content": [
@@ -89,10 +185,58 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-02T10:23:27.387621193",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-02-02T10:23:27.387621193"
+        }
+    },
+    "test-picard-collectmultiplemetrics-cram - stub": {
+        "content": [
+            {
+                "metrics": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        [
+                            "test.CollectMultipleMetrics.alignment_summary_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.base_distribution_by_cycle_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.insert_size_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_by_cycle_metrics:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_distribution_metrics:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "pdf": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        [
+                            "test.CollectMultipleMetrics.base_distribution_by_cycle.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.insert_size_histogram.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_by_cycle.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.quality_distribution.pdf:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "test.CollectMultipleMetrics.read_length_histogram.pdf:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "versions_picard": [
+                    [
+                        "PICARD_COLLECTMULTIPLEMETRICS",
+                        "picard",
+                        "3.4.0"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-02-20T10:32:57.11686549",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/picard/collectwgsmetrics/main.nf b/modules/nf-core/picard/collectwgsmetrics/main.nf
index b8d1ee42..8048b2b6 100644
--- a/modules/nf-core/picard/collectwgsmetrics/main.nf
+++ b/modules/nf-core/picard/collectwgsmetrics/main.nf
@@ -1,11 +1,11 @@
 process PICARD_COLLECTWGSMETRICS {
-    tag "$meta.id"
+    tag "${meta.id}"
     label 'process_single'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/08/0861295baa7c01fc593a9da94e82b44a729dcaf8da92be8e565da109aa549b25/data' :
-        'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6' }"
+    container "${workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/08/0861295baa7c01fc593a9da94e82b44a729dcaf8da92be8e565da109aa549b25/data'
+        : 'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6'}"
 
     input:
     tuple val(meta), path(bam), path(bai) ,path(fasta) ,path(fai), path(dict)
@@ -13,32 +13,33 @@ process PICARD_COLLECTWGSMETRICS {
 
     output:
     tuple val(meta), path("*_metrics"), emit: metrics
-    tuple val("${task.process}"), val('picard'), eval("picard CollectWgsMetrics --version 2>&1 | sed -n 's/^Version:*//p'"), topic: versions, emit: versions_picard
+    tuple val("${task.process}"), val('picard'), eval("picard CollectWgsMetrics --version 2>&1 | sed -n 's/.*Version://p'"), topic: versions, emit: versions_picard
 
     when:
     task.ext.when == null || task.ext.when
 
     script:
-    def args      = task.ext.args ?: ''
-    def prefix    = task.ext.prefix ?: "${meta.id}"
+    def args = task.ext.args ?: ''
+    def prefix = task.ext.prefix ?: "${meta.id}"
     def avail_mem = 3072
-    def interval  = intervallist ? "--INTERVALS ${intervallist}" : ''
+    def interval = intervallist ? "--INTERVALS ${intervallist}" : ''
     if (!task.memory) {
-        log.info '[Picard CollectWgsMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.'
-    } else {
-        avail_mem = (task.memory.mega*0.8).intValue()
+        log.info('[Picard CollectWgsMetrics] Available memory not known - defaulting to 3GB. Specify process memory requirements to change this.')
+    }
+    else {
+        avail_mem = (task.memory.mega * 0.8).intValue()
     }
     """
     export TMP=\$PWD
     picard \\
         -Xmx${avail_mem}M \\
         CollectWgsMetrics \\
-        $args \\
-        --INPUT $bam \\
+        ${args} \\
+        --INPUT ${bam} \\
         --OUTPUT ${prefix}.CollectWgsMetrics.coverage_metrics \\
         --REFERENCE_SEQUENCE ${fasta} \\
         --TMP_DIR . \\
-        $interval
+        ${interval}
 
     """
 
@@ -46,6 +47,5 @@ process PICARD_COLLECTWGSMETRICS {
     def prefix = task.ext.prefix ?: "${meta.id}"
     """
     touch ${prefix}.CollectWgsMetrics.coverage_metrics
-
     """
 }
diff --git a/modules/nf-core/picard/collectwgsmetrics/meta.yml b/modules/nf-core/picard/collectwgsmetrics/meta.yml
index c59811d5..c5afe2e7 100644
--- a/modules/nf-core/picard/collectwgsmetrics/meta.yml
+++ b/modules/nf-core/picard/collectwgsmetrics/meta.yml
@@ -76,10 +76,9 @@ output:
       - picard:
           type: string
           description: The tool name
-      - "picard CollectWgsMetrics --version 2>&1 | sed -n 's/^Version:*//p'":
+      - "picard CollectWgsMetrics --version 2>&1 | sed -n 's/.*Version://p'":
           type: string
           description: The command used to generate the version of the tool
-
 topics:
   versions:
     - - ${task.process}:
@@ -88,7 +87,7 @@ topics:
       - picard:
           type: string
           description: The tool name
-      - "picard CollectWgsMetrics --version 2>&1 | sed -n 's/^Version:*//p'":
+      - "picard CollectWgsMetrics --version 2>&1 | sed -n 's/.*Version://p'":
           type: string
           description: The command used to generate the version of the tool
 authors:
diff --git a/modules/nf-core/picard/collectwgsmetrics/picard-collectwgsmetrics.diff b/modules/nf-core/picard/collectwgsmetrics/picard-collectwgsmetrics.diff
index c1389e4e..5de262d0 100644
--- a/modules/nf-core/picard/collectwgsmetrics/picard-collectwgsmetrics.diff
+++ b/modules/nf-core/picard/collectwgsmetrics/picard-collectwgsmetrics.diff
@@ -4,33 +4,37 @@ Changes in component 'nf-core/picard/collectwgsmetrics'
 Changes in 'picard/collectwgsmetrics/main.nf':
 --- modules/nf-core/picard/collectwgsmetrics/main.nf
 +++ modules/nf-core/picard/collectwgsmetrics/main.nf
-@@ -8,9 +8,7 @@
-         'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6' }"
+@@ -8,10 +8,8 @@
+         : 'community.wave.seqera.io/library/picard:3.4.0--e9963040df0a9bf6'}"
  
      input:
 -    tuple val(meta), path(bam), path(bai)
 -    tuple val(meta2), path(fasta)
 -    tuple val(meta3), path(fai)
+-    path intervallist
 +    tuple val(meta), path(bam), path(bai) ,path(fasta) ,path(fai), path(dict)
-     path  intervallist
++    path  intervallist
  
      output:
-@@ -31,6 +29,7 @@
-         avail_mem = (task.memory.mega*0.8).intValue()
+     tuple val(meta), path("*_metrics"), emit: metrics
+@@ -32,6 +30,7 @@
+         avail_mem = (task.memory.mega * 0.8).intValue()
      }
      """
 +    export TMP=\$PWD
      picard \\
          -Xmx${avail_mem}M \\
          CollectWgsMetrics \\
-@@ -38,6 +37,7 @@
-         --INPUT $bam \\
+@@ -39,7 +38,9 @@
+         --INPUT ${bam} \\
          --OUTPUT ${prefix}.CollectWgsMetrics.coverage_metrics \\
          --REFERENCE_SEQUENCE ${fasta} \\
 +        --TMP_DIR . \\
-         $interval
- 
+         ${interval}
++
      """
+ 
+     stub:
 
 'modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test.snap' is unchanged
 'modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test' is unchanged
diff --git a/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test b/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test
index d6a60e6b..1bda5980 100644
--- a/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test
+++ b/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test
@@ -16,19 +16,18 @@ nextflow_process {
             process {
                 """
                 input[0] = [ [ id:'test', single_end:false ], // meta map
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true),
-				]
-				input[1] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
-				]
-				input[2] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
-				]
-				input[3] = []
-
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true),
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ]
+                input[3] = []
                 """
             }
         }
@@ -37,10 +36,9 @@ nextflow_process {
             assertAll(
                 { assert process.success },
                 { assert snapshot(
-                    file(process.out.metrics[0][1]).text.contains('coverage	high_quality_coverage_count'),
+                    file(process.out.metrics[0][1]).text.contains('coverage    high_quality_coverage_count'),
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
     }
@@ -51,19 +49,18 @@ nextflow_process {
             process {
                 """
                 input[0] = [ [ id:'test', single_end:false ], // meta map
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
-				    []
-				]
-				input[1] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
-				]
-				input[2] = [
-				    [id:'genome'],
-				    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
-				]
-				input[3] = file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/picard/baits.interval_list', checkIfExists: true)
-
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    []
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ]
+                input[3] = file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/picard/baits.interval_list', checkIfExists: true)
                 """
             }
         }
@@ -72,12 +69,70 @@ nextflow_process {
             assertAll(
                 { assert process.success },
                 { assert snapshot(
-                    file(process.out.metrics[0][1]).text.contains('coverage	high_quality_coverage_count'),
+                    file(process.out.metrics[0][1]).text.contains('coverage    high_quality_coverage_count'),
                     process.out.findAll { key, val -> key.startsWith("versions") }
-                    ).match()
-                }
+                ).match()}
             )
         }
     }
 
+    test("test-picard-collectwgsmetrics - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = [ [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true),
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ]
+                input[3] = []
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(sanitizeOutput(process.out)).match() }
+            )
+        }
+    }
+
+    test("test-picard-collectwgsmetrics-with-interval - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = [ [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                    []
+                ]
+                input[1] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                input[2] = [
+                    [id:'genome'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ]
+                input[3] = file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/picard/baits.interval_list', checkIfExists: true)
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(sanitizeOutput(process.out)).match() }
+            )
+        }
+    }
 }
diff --git a/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test.snap b/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test.snap
index 375c2ef9..79f1145f 100644
--- a/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test.snap
+++ b/modules/nf-core/picard/collectwgsmetrics/tests/main.nf.test.snap
@@ -1,7 +1,34 @@
 {
+    "test-picard-collectwgsmetrics-with-interval - stub": {
+        "content": [
+            {
+                "metrics": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.CollectWgsMetrics.coverage_metrics:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "versions_picard": [
+                    [
+                        "PICARD_COLLECTWGSMETRICS",
+                        "picard",
+                        "3.4.0"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-02-20T10:35:04.636691319",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
+    },
     "test-picard-collectwgsmetrics-with-interval": {
         "content": [
-            true,
+            false,
             {
                 "versions_picard": [
                     [
@@ -12,15 +39,42 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-20T10:34:45.059411647",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
+    },
+    "test-picard-collectwgsmetrics - stub": {
+        "content": [
+            {
+                "metrics": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.CollectWgsMetrics.coverage_metrics:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "versions_picard": [
+                    [
+                        "PICARD_COLLECTWGSMETRICS",
+                        "picard",
+                        "3.4.0"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-02-20T10:34:54.347278951",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-02-02T14:52:55.091876466"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "test-picard-collectwgsmetrics": {
         "content": [
-            true,
+            false,
             {
                 "versions_picard": [
                     [
@@ -31,10 +85,10 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-20T10:34:25.744978033",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.2"
-        },
-        "timestamp": "2026-02-02T14:52:11.334274481"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/star/align/meta.yml b/modules/nf-core/star/align/meta.yml
index d1441570..3df5cfe7 100644
--- a/modules/nf-core/star/align/meta.yml
+++ b/modules/nf-core/star/align/meta.yml
@@ -49,12 +49,6 @@ input:
   - star_ignore_sjdbgtf:
       type: boolean
       description: Ignore annotation GTF file
-  - seq_platform:
-      type: string
-      description: Sequencing platform
-  - seq_center:
-      type: string
-      description: Sequencing center
 output:
   log_final:
     - - meta:
diff --git a/modules/nf-core/star/align/star-align.diff b/modules/nf-core/star/align/star-align.diff
index 0c9c6621..07d96b66 100644
--- a/modules/nf-core/star/align/star-align.diff
+++ b/modules/nf-core/star/align/star-align.diff
@@ -4,7 +4,7 @@ Changes in component 'nf-core/star/align'
 Changes in 'star/align/main.nf':
 --- modules/nf-core/star/align/main.nf
 +++ modules/nf-core/star/align/main.nf
-@@ -8,10 +8,7 @@
+@@ -8,10 +8,9 @@
          'community.wave.seqera.io/library/htslib_samtools_star_gawk:ae438e9a604351a4' }"
  
      input:
@@ -13,18 +13,24 @@ Changes in 'star/align/main.nf':
 -    tuple val(meta3), path(gtf)
 -    val star_ignore_sjdbgtf
 +    tuple val(meta), path(reads, stageAs: "input*/*"), path(index), path(gtf)
-     val seq_platform
-     val seq_center
++    val seq_platform
++    val seq_center
  
-@@ -46,7 +43,7 @@
+     output:
+     tuple val(meta), path('*Log.final.out')   , emit: log_final
+@@ -44,8 +43,10 @@
      def reads1 = []
      def reads2 = []
      meta.single_end ? [reads].flatten().each{ read -> reads1 << read} : reads.eachWithIndex{ v, ix -> ( ix & 1 ? reads2 : reads1) << v }
 -    def ignore_gtf      = star_ignore_sjdbgtf ? '' : "--sjdbGTFfile $gtf"
+-    attrRG          = args.contains("--outSAMattrRGline") ? "" : "--outSAMattrRGline 'ID:$prefix' 'SM:$prefix'"
 +    def ignore_gtf      = gtf ? "--sjdbGTFfile $gtf" : ''
-     def seq_platform_arg  = seq_platform ? "'PL:$seq_platform'" : ""
-     def seq_center_arg    = seq_center ? "'CN:$seq_center'" : ""
-     attrRG          = args.contains("--outSAMattrRGline") ? "" : "--outSAMattrRGline 'ID:$prefix' $seq_center_arg 'SM:$prefix' $seq_platform_arg"
++    def seq_platform_arg  = seq_platform ? "'PL:$seq_platform'" : ""
++    def seq_center_arg    = seq_center ? "'CN:$seq_center'" : ""
++    attrRG          = args.contains("--outSAMattrRGline") ? "" : "--outSAMattrRGline 'ID:$prefix' $seq_center_arg 'SM:$prefix' $seq_platform_arg"
+     def out_sam_type    = (args.contains('--outSAMtype')) ? '' : '--outSAMtype BAM Unsorted'
+     mv_unsorted_bam = (args.contains('--outSAMtype BAM Unsorted SortedByCoordinate')) ? "mv ${prefix}.Aligned.out.bam ${prefix}.Aligned.unsort.out.bam" : ''
+     """
 
 'modules/nf-core/star/align/tests/main.nf.test.snap' is unchanged
 'modules/nf-core/star/align/tests/nextflow.arriba.config' is unchanged
diff --git a/modules/nf-core/star/align/tests/main.nf.test b/modules/nf-core/star/align/tests/main.nf.test
index da0cc07e..d7c6ccbd 100644
--- a/modules/nf-core/star/align/tests/main.nf.test
+++ b/modules/nf-core/star/align/tests/main.nf.test
@@ -43,8 +43,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -108,8 +106,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -173,8 +169,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -237,8 +231,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -304,8 +296,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -367,8 +357,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -419,8 +407,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -471,8 +457,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -523,8 +507,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
@@ -577,8 +559,6 @@ nextflow_process {
                     [ file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.gtf', checkIfExists: true) ]
                 ])
                 input[3] = false
-                input[4] = 'illumina'
-                input[5] = false
                 """
             }
         }
diff --git a/nextflow.config b/nextflow.config
index a5ceb27d..c3e162e8 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -14,6 +14,7 @@ params {
 
     // References
     genome                       = null
+    genomes                      = [:]
     igenomes_base                = '/references/'
     igenomes_ignore              = false
 
@@ -64,7 +65,6 @@ includeConfig !params.igenomes_ignore ? 'conf/igenomes.config' : 'conf/igenomes_
 
 profiles {
     debug {
-        dumpHashes                                   = true
         process.beforeScript                         = 'echo $HOSTNAME'
         cleanup                                      = false
         nextflow.enable.configProcessNamesValidation = true
@@ -108,7 +108,7 @@ profiles {
         singularity.ociAutoPull = true
         wave.enabled            = true
         wave.freeze             = true
-        wave.strategy           = 'conda,container'
+        wave.strategy           = ["conda", "container"]
     }
     emulate_amd64 {
         docker.runOptions = '-u $(id -u):$(id -g) --platform=linux/amd64'
@@ -165,7 +165,7 @@ profiles {
         singularity.ociAutoPull = true
         wave.enabled            = true
         wave.freeze             = true
-        wave.strategy           = 'conda,container'
+        wave.strategy           = ["conda", "container"]
     }
     gpu {
         docker.runOptions      = '-u $(id -u):$(id -g) --gpus all'
@@ -211,7 +211,7 @@ env {
 }
 
 // Set bash options
-process.shell = ["bash", "-C", "-e", "-u", "-o", "pipefail"]
+process.shell = ["bash", "-C", "-e", "-u", "-o", "pipefail"].join(' ')
 
 // Disable process selector warnings by default. Use debug profile to enable warnings.
 nextflow.enable.configProcessNamesValidation = false
@@ -234,7 +234,7 @@ trace {
 dag {
     enabled   = true
     overwrite = true
-    file      = "${params.outdir}/pipeline_info/pipeline_dag_${params.trace_report_suffix}.html"
+    file      = "${params.outdir}/pipeline_info/pipeline_dag_${params.trace_report_suffix}.mmd"
 }
 
 manifest {
@@ -262,7 +262,7 @@ manifest {
     mainScript      = 'main.nf'
     defaultBranch   = 'main'
     nextflowVersion = '!>=25.10.0'
-    version         = '3.0.0dev'
+    version         = '3.0.0'
     doi             = ''
 }
 
diff --git a/nf-test.config b/nf-test.config
index c5d343b4..bda04274 100644
--- a/nf-test.config
+++ b/nf-test.config
@@ -12,7 +12,7 @@ config {
     ignore     = ['modules/nf-core/**/tests/*', 'subworkflows/nf-core/**/tests/*']
 
     // run all test with defined profile(s) from the main nextflow.config
-    profile    = "test"
+    profile    = "test,s3_ugent"
 
     // list of filenames or patterns that should be trigger a full test run
     triggers   = ['nextflow.config', 'nf-test.config', 'conf/test.config', 'tests/nextflow.config', 'tests/.nftignore']
diff --git a/ro-crate-metadata.json b/ro-crate-metadata.json
index 1d3d46af..40119c7b 100644
--- a/ro-crate-metadata.json
+++ b/ro-crate-metadata.json
@@ -21,9 +21,9 @@
         {
             "@id": "./",
             "@type": "Dataset",
-            "creativeWorkStatus": "InProgress",
-            "datePublished": "2026-02-09T13:16:20+00:00",
-            "description": "# nf-cmgg/preprocessing\n\n[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/nf-cmgg/preprocessing)\n[![GitHub Actions CI Status](https://github.com/nf-cmgg/preprocessing/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-cmgg/preprocessing/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-cmgg/preprocessing/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-cmgg/preprocessing/actions/workflows/linting.yml)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.0-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-3.5.1-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.5.1)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-cmgg/preprocessing)\n\n## Introduction\n\n**nf-cmgg/preprocessing** is a bioinformatics pipeline that demultiplexes and aligns raw sequencing data.\nIt also performs basic QC and coverage analysis.\n\nThe pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.\n\nSteps inlcude:\n\n1. Demultiplexing using [`BCLconvert`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html)\n2. Read QC and trimming using [`fastp`](https://github.com/OpenGene/fastp)\n3. Alignment using either [`bwa`](https://github.com/lh3/bwa), [`bwa-mem2`](https://github.com/bwa-mem2/bwa-mem2), [`bowtie2`](https://github.com/BenLangmead/bowtie2), [`dragmap`](https://github.com/Illumina/DRAGMAP), [`snap`](https://github.com/amplab/snap) or [`strobe`](https://github.com/ksahlin/strobealign) for DNA-seq and [`STAR`](https://github.com/alexdobin/STAR) for RNA-seq\n4. Duplicate marking using [`bamsormadup`](https://gitlab.com/german.tischler/biobambam2) or [`samtools markdup`](http://www.htslib.org/doc/samtools-markdup.html)\n5. Coverage analysis using [`mosdepth`](https://github.com/brentp/mosdepth) and [`samtools coverage`](http://www.htslib.org/doc/samtools-coverage.html)\n6. Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollecHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)\n7. QC aggregation using [`multiqc`](https://multiqc.info/)\n\n![metro map](docs/images/metro_map.png)\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nThe full documentation can be found [here](docs/README.md)\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv` for fastq inputs:\n\n```csv\nid,samplename,organism,library,aligner,fastq_1,fastq_2\nsample1,sample1,Homo sapiens,Library_Name,bwamem,reads1.fq.gz,reads2.fq.gz\n```\n\n`samplesheet.csv` for flowcell inputs:\n\n```csv\nid,samplesheet,lane,flowcell,sample_info\nflowcell_id,/path/to/illumina_samplesheet.csv,1,/path/to/sequencer_uploaddir,/path/to/sampleinfo.csv\n```\n\n`sampleinfo.csv` for use with flowcell inputs:\n\n```csv\nsamplename,library,organism,tag,aligner\nfc_sample1,test,Homo sapiens,WES,bwamem\n```\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-cmgg/preprocessing \\\n   -profile <docker/singularity/.../institute> \\\n   --igenomes_base /path/to/genomes \\\n   --input samplesheet.csv \\\n   --outdir <OUTDIR>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_;\n> see [docs](https://nf-co.re/usage/configuration#custom-configuration-files).\n\n## Credits\n\nnf-cmgg/preprocessing was originally written by the CMGG ICT team.\n\n## Support\n\nThis pipeline uses code and infrastructure developed and maintained by the [nf-core](https://nf-co.re) community, reused here under the [MIT license](https://github.com/nf-core/tools/blob/master/LICENSE).\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
+            "creativeWorkStatus": "Stable",
+            "datePublished": "2026-03-04T08:40:34+00:00",
+            "description": "# nf-cmgg/preprocessing\n\n[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/nf-cmgg/preprocessing)\n[![GitHub Actions CI Status](https://github.com/nf-cmgg/preprocessing/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-cmgg/preprocessing/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-cmgg/preprocessing/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-cmgg/preprocessing/actions/workflows/linting.yml)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.0-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-3.5.1-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.5.1)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-cmgg/preprocessing)\n\n## Introduction\n\n**nf-cmgg/preprocessing** is a bioinformatics pipeline that demultiplexes and aligns raw sequencing data.\nIt also performs basic QC and coverage analysis.\n\nThe pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.\n\nSteps inlcude:\n\n1. Demultiplexing using [`BCLconvert`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html)\n2. Read QC and trimming using [`fastp`](https://github.com/OpenGene/fastp)\n3. Alignment using either [`bwa`](https://github.com/lh3/bwa), [`bwa-mem2`](https://github.com/bwa-mem2/bwa-mem2), [`bowtie2`](https://github.com/BenLangmead/bowtie2), [`dragmap`](https://github.com/Illumina/DRAGMAP), [`snap`](https://github.com/amplab/snap) or [`strobe`](https://github.com/ksahlin/strobealign) for DNA-seq and [`STAR`](https://github.com/alexdobin/STAR) for RNA-seq\n4. Duplicate marking using [`bamsormadup`](https://gitlab.com/german.tischler/biobambam2) or [`samtools markdup`](http://www.htslib.org/doc/samtools-markdup.html)\n5. Coverage analysis using [`mosdepth`](https://github.com/brentp/mosdepth) and [`samtools coverage`](http://www.htslib.org/doc/samtools-coverage.html)\n6. Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollecHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)\n7. QC aggregation using [`multiqc`](https://multiqc.info/)\n\n![metro map](docs/images/metro_map.png)\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nThe full documentation can be found [here](docs/README.md)\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv` for fastq inputs:\n\n```csv\nid,samplename,organism,library,aligner,fastq_1,fastq_2\nsample1,sample1,Homo sapiens,Library_Name,bwamem,reads1.fq.gz,reads2.fq.gz\n```\n\n`samplesheet.csv` for flowcell inputs:\n\n```csv\nid,samplesheet,lane,flowcell,sample_info\nflowcell_id,/path/to/illumina_samplesheet.csv,1,/path/to/sequencer_uploaddir,/path/to/sampleinfo.csv\n```\n\n`sampleinfo.csv` for use with flowcell inputs:\n\n```csv\nsamplename,library,organism,tag,aligner\nfc_sample1,test,Homo sapiens,WES,bwamem\n```\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-cmgg/preprocessing \\\n   -profile <docker/singularity/.../institute> \\\n   --igenomes_base /path/to/genomes \\\n   --input samplesheet.csv \\\n   --outdir <OUTDIR>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_;\n> see [docs](https://nf-co.re/usage/configuration#custom-configuration-files).\n\n## Credits\n\nnf-cmgg/preprocessing was originally written by the CMGG ICT team.\n\n## Support\n\nThis pipeline uses code and infrastructure developed and maintained by the [nf-core](https://nf-co.re) community, reused here under the [MIT license](https://github.com/nf-core/tools/blob/master/LICENSE).\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
             "hasPart": [
                 {
                     "@id": "main.nf"
@@ -105,7 +105,7 @@
             },
             "mentions": [
                 {
-                    "@id": "#e939b96d-700d-40a2-9ff8-added4e0ade4"
+                    "@id": "#a7d7e3e1-4abb-45b6-8465-8056924d5808"
                 }
             ],
             "name": "nf-cmgg/preprocessing"
@@ -134,14 +134,14 @@
             ],
             "creator": [
                 {
-                    "@id": "https://orcid.org/0000-0003-2555-3114"
+                    "@id": "https://orcid.org/0009-0003-5619-1555"
                 },
                 {
-                    "@id": "https://orcid.org/0009-0003-5619-1555"
+                    "@id": "https://orcid.org/0000-0003-2555-3114"
                 }
             ],
             "dateCreated": "",
-            "dateModified": "2026-02-09T14:16:20Z",
+            "dateModified": "2026-03-04T09:40:34Z",
             "dct:conformsTo": "https://bioschemas.org/profiles/ComputationalWorkflow/1.0-RELEASE/",
             "image": {
                 "@id": "docs/images/metro_map.png"
@@ -169,10 +169,10 @@
             },
             "url": [
                 "https://github.com/nf-cmgg/preprocessing",
-                "https://nf-co.re/nf-cmgg/preprocessing/dev/"
+                "https://nf-co.re/nf-cmgg/preprocessing/3.0.0/"
             ],
             "version": [
-                "3.0.0dev"
+                "3.0.0"
             ]
         },
         {
@@ -196,11 +196,11 @@
             "name": "Workflow diagram"
         },
         {
-            "@id": "#e939b96d-700d-40a2-9ff8-added4e0ade4",
+            "@id": "#a7d7e3e1-4abb-45b6-8465-8056924d5808",
             "@type": "TestSuite",
             "instance": [
                 {
-                    "@id": "#a6c6a5a5-b7e8-4825-8dce-3f467dbc2c91"
+                    "@id": "#61347af5-7167-4ebd-a8c0-2b36f46308d1"
                 }
             ],
             "mainEntity": {
@@ -209,7 +209,7 @@
             "name": "Test suite for nf-cmgg/preprocessing"
         },
         {
-            "@id": "#a6c6a5a5-b7e8-4825-8dce-3f467dbc2c91",
+            "@id": "#61347af5-7167-4ebd-a8c0-2b36f46308d1",
             "@type": "TestInstance",
             "name": "GitHub Actions workflow for testing nf-cmgg/preprocessing",
             "resource": "repos/nf-cmgg/preprocessing/actions/workflows/nf-test.yml",
@@ -342,17 +342,17 @@
             "name": "nf-core",
             "url": "https://nf-co.re/"
         },
-        {
-            "@id": "https://orcid.org/0000-0003-2555-3114",
-            "@type": "Person",
-            "email": "11850640+matthdsm@users.noreply.github.com",
-            "name": "Matthias De Smet"
-        },
         {
             "@id": "https://orcid.org/0009-0003-5619-1555",
             "@type": "Person",
             "email": "101190534+nvnieuwk@users.noreply.github.com",
             "name": "Nicolas Vannieuwkerke"
+        },
+        {
+            "@id": "https://orcid.org/0000-0003-2555-3114",
+            "@type": "Person",
+            "email": "11850640+matthdsm@users.noreply.github.com",
+            "name": "Matthias De Smet"
         }
     ]
 }
\ No newline at end of file
diff --git a/subworkflows/local/utils_nfcmgg_preprocessing_pipeline/main.nf b/subworkflows/local/utils_nfcmgg_preprocessing_pipeline/main.nf
new file mode 100644
index 00000000..a20d0849
--- /dev/null
+++ b/subworkflows/local/utils_nfcmgg_preprocessing_pipeline/main.nf
@@ -0,0 +1,95 @@
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    SUBWORKFLOWS
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+// Mock subworkflow to please linting
+workflow UTILS_NFCMGG_PREPROCESSING_PIPELINE {
+}
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    FUNCTIONS
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+//
+// Generate readgroup information from FASTQ header
+//
+def getReadgroupFromFastq(fastq, SM, LB, CN) {
+    // expected format:
+    // xx:yy:FLOWCELLID:LANE:... (seven fields)
+    // or
+    // FLOWCELLID:LANE:xx:... (five fields)
+    def line
+    fastq.withInputStream { fq ->
+        def isGzip = fastq.name.toString().endsWith('.gz')
+        def stream = isGzip ? new java.util.zip.GZIPInputStream(fq) as InputStream : fq as InputStream
+        def decoder = new InputStreamReader(stream, 'ASCII')
+        def buffered = new BufferedReader(decoder)
+        line = buffered.readLine()
+    }
+    assert line.startsWith('@')
+    line = line.substring(1)
+    def fields = line.split(':')
+    def rg = [:]
+    rg.LB = LB ?: ''
+    rg.CN = CN ?: ''
+    rg.PL = 'ILLUMINA'
+    rg.SM = SM ?: fastq.name.toString() - ~/_R[0-9]_001.*$/
+    if (fields.size() >= 7) {
+        // CASAVA 1.8+ format, from  https://support.illumina.com/help/BaseSpace_OLH_009008/Content/Source/Informatics/BS/FileFormat_FASTQ-files_swBS.htm
+        // "@<instrument>:<run number>:<flowcell ID>:<lane>:<tile>:<x-pos>:<y-pos>:<UMI> <read>:<is filtered>:<control number>:<index>"
+        // def sequencer_serial = fields[0]
+        // def run_number       = fields[1]
+        def fcid = fields[2]
+        def lane = fields[3]
+        def index = fields[-1] ==~ /^[GATCN+-]+$/ ? fields[-1] : ''
+        rg.ID = [index ?: fcid, lane].join('.')
+        rg.PU = [fcid, lane].join('.')
+    }
+    else if (fields.size() == 5) {
+        def fcid = fields[0]
+        def lane = fields[1]
+        rg.ID = [fcid, lane].join('.')
+        rg.PU = [fcid, lane].join('.')
+    }
+    return rg
+}
+
+//
+// Generate readgroup from bclconvert outputs
+//
+def getReadgroupsFromBclconvert(ch_fastq_list_csv, ch_fastq) {
+    return ch_fastq_list_csv
+        .join(ch_fastq, by: [0])
+        .map { meta, csv_file, fastq_list ->
+            def meta_fastq = []
+            csv_file
+                .splitCsv(header: true)
+                .each { row ->
+                    // Create the readgroup tuple
+                    // RGID,RGSM,RGLB,Lane,Read1File,Read2File
+                    def rg = [:]
+                    // row.RGID is index1.index2.lane
+                    rg.ID = row.RGID
+                    // RGPU is a custom column in the samplesheet containing the flowcell ID
+                    rg.PU = row.RGPU ? row.RGPU : meta.id + "." + row.Lane
+                    rg.SM = row.RGSM
+                    rg.LB = row.RGLB ? row.RGLB : ""
+                    rg.PL = "ILLUMINA"
+
+                    // dereference the fastq files in the csv
+                    def fastq1 = fastq_list.find { fq -> file(fq).name == file(row.Read1File).name }
+                    def fastq2 = row.Read2File ? fastq_list.find { fq -> file(fq).name == file(row.Read2File).name } : null
+
+                    // set fastq metadata
+                    def new_meta = meta + [id: fastq1.getSimpleName().toString() - ~/_R[0-9]_001.*$/, readgroup: rg, single_end: !fastq2]
+
+                    meta_fastq << [new_meta, fastq2 ? [fastq1, fastq2] : [fastq1]]
+                }
+            return meta_fastq
+        }
+        .flatMap()
+}
diff --git a/subworkflows/local/utils_nfcmgg_preprocessing_pipeline/meta.yml b/subworkflows/local/utils_nfcmgg_preprocessing_pipeline/meta.yml
new file mode 100644
index 00000000..e69de29b
diff --git a/subworkflows/nf-core/fastq_align_dna/fastq_align_dna.diff b/subworkflows/nf-core/fastq_align_dna/fastq_align_dna.diff
index f2a4356b..2f585e04 100644
--- a/subworkflows/nf-core/fastq_align_dna/fastq_align_dna.diff
+++ b/subworkflows/nf-core/fastq_align_dna/fastq_align_dna.diff
@@ -17,9 +17,9 @@ Changes in 'fastq_align_dna/main.nf':
  
      main:
  
--        ch_bam_index    = Channel.empty()
--        ch_bam          = Channel.empty()
--        ch_reports      = Channel.empty()
+-        ch_bam_index    = channel.empty()
+-        ch_bam          = channel.empty()
+-        ch_reports      = channel.empty()
 +    ch_bam_index = channel.empty()
 +    ch_bam = channel.empty()
 +    ch_reports = channel.empty()
diff --git a/tests/config/igenomes_test.config b/tests/config/igenomes_test.config
index 5ecb24af..e71fec6f 100644
--- a/tests/config/igenomes_test.config
+++ b/tests/config/igenomes_test.config
@@ -11,12 +11,3 @@ params {
         }
     }
 }
-
-aws {
-    client {
-        endpoint          = "https://s3.ugent.be"
-        protocol          = "https"
-        s3PathStyleAccess = true
-        connectionTimeout = 60000
-    }
-}
diff --git a/tests/config/nf-test.config b/tests/config/nf-test.config
index c24e5c2f..88f7efd5 100644
--- a/tests/config/nf-test.config
+++ b/tests/config/nf-test.config
@@ -5,12 +5,3 @@ process {
         time: 6.h,
     ]
 }
-
-aws {
-    client {
-        endpoint          = "https://s3.ugent.be"
-        protocol          = "https"
-        s3PathStyleAccess = true
-        connectionTimeout = 60000
-    }
-}
diff --git a/tests/default.nf.test b/tests/default.nf.test
index 66a790d4..d32cc960 100644
--- a/tests/default.nf.test
+++ b/tests/default.nf.test
@@ -7,43 +7,11 @@ nextflow_pipeline {
     tag "pipeline"
     tag "pipeline/main"
 
-    test("main - fastq input") {
+    test("main") {
 
         when {
             params {
-                input = "${projectDir}/tests/inputs/fastq.yml"
-                igenomes_base = "s3://reference-data/genomes"
-                outdir = "$outputDir"
-            }
-        }
-
-        then {
-            assert workflow.success
-        }
-
-    }
-
-    test("main - flowcell input") {
-
-        when {
-            params {
-                input = "${projectDir}/tests/inputs/flowcell.yml"
-                igenomes_base = "s3://reference-data/genomes"
-                outdir = "$outputDir"
-            }
-        }
-
-        then {
-            assert workflow.success
-        }
-
-    }
-
-    test("main - fastq RNA input") {
-
-        when {
-            params {
-                input = "${projectDir}/tests/inputs/fastq_rna.yml"
+                input = "${projectDir}/tests/inputs/test.yml"
                 igenomes_base = "s3://reference-data/genomes"
                 outdir = "$outputDir"
             }
diff --git a/tests/inputs/fastq.yml b/tests/inputs/fastq.yml
deleted file mode 100644
index 8195b58b..00000000
--- a/tests/inputs/fastq.yml
+++ /dev/null
@@ -1,22 +0,0 @@
----
-# fastq inputs
-- id: sample1_L001
-  samplename: fastq_paired1
-  library: test_library
-  organism: Homo sapiens
-  tag: WES
-  aligner: bwamem
-  markdup: bamsormadup
-  run_coverage: true
-  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R1.fastq.gz
-  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R2.fastq.gz
-- id: sample1_L002
-  samplename: fastq_paired1
-  library: test_library
-  organism: Homo sapiens
-  tag: WES
-  aligner: bwamem
-  markdup: bamsormadup
-  run_coverage: true
-  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R1.fastq.gz
-  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R2.fastq.gz
diff --git a/tests/inputs/fastq_rna.yml b/tests/inputs/fastq_rna.yml
deleted file mode 100644
index 3945eb53..00000000
--- a/tests/inputs/fastq_rna.yml
+++ /dev/null
@@ -1,24 +0,0 @@
----
-# fastq inputs
-- id: sample1_L001
-  samplename: fastq_paired1
-  library: test_library
-  organism: Homo sapiens
-  tag: WES
-  sample_type: RNA
-  aligner: star
-  markdup: bamsormadup
-  run_coverage: true
-  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R1.fastq.gz
-  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R2.fastq.gz
-- id: sample1_L002
-  samplename: fastq_paired1
-  library: test_library
-  organism: Homo sapiens
-  tag: WES
-  sample_type: RNA
-  aligner: star
-  markdup: bamsormadup
-  run_coverage: true
-  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R1.fastq.gz
-  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R2.fastq.gz
diff --git a/tests/inputs/flowcell.yml b/tests/inputs/flowcell.yml
deleted file mode 100644
index 4711c5b9..00000000
--- a/tests/inputs/flowcell.yml
+++ /dev/null
@@ -1,6 +0,0 @@
----
-- id: 200624_A00834_0183_BHMTFYDRXX
-  samplesheet: https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleSheet_2.csv
-  lane: 1
-  flowcell: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/200624_A00834_0183_BHMTFYDRXX.tar.gz
-  sample_info: https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleInfo_2.json
diff --git a/tests/inputs/test.yml b/tests/inputs/test.yml
new file mode 100644
index 00000000..3a432b4b
--- /dev/null
+++ b/tests/inputs/test.yml
@@ -0,0 +1,51 @@
+---
+# flowcell inputs
+- id: 200624_A00834_0183_BHMTFYDRXX
+  samplesheet: https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleSheet_2.csv
+  lane: 1
+  flowcell: s3://test-data/genomics/homo_sapiens/illumina/bcl/
+  sample_info: https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleInfo_2.json
+# DNA fastq inputs
+- id: DNA1_L001
+  samplename: DNA_paired1
+  library: test_library
+  organism: Homo sapiens
+  tag: WES
+  aligner: bwamem
+  markdup: bamsormadup
+  run_coverage: true
+  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R1.fastq.gz
+  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R2.fastq.gz
+- id: DNA1_L002
+  samplename: DNA_paired1
+  library: test_library
+  organism: Homo sapiens
+  tag: WES
+  aligner: bwamem
+  markdup: bamsormadup
+  run_coverage: true
+  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R1.fastq.gz
+  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R2.fastq.gz
+# RNA fastq inputs
+- id: RNA1_L001
+  samplename: RNA_paired1
+  library: test_library
+  organism: Homo sapiens
+  tag: WES
+  sample_type: RNA
+  aligner: star
+  markdup: bamsormadup
+  run_coverage: true
+  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R1.fastq.gz
+  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R2.fastq.gz
+- id: RNA1_L002
+  samplename: RNA_paired1
+  library: test_library
+  organism: Homo sapiens
+  tag: WES
+  sample_type: RNA
+  aligner: star
+  markdup: bamsormadup
+  run_coverage: true
+  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R1.fastq.gz
+  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/main/data/genomics/homo_sapiens/illumina/fastq/test_R2.fastq.gz
diff --git a/tests/workflows/preprocessing.nf.test b/tests/workflows/preprocessing.nf.test
index 4d5e52cc..d8bac5f5 100644
--- a/tests/workflows/preprocessing.nf.test
+++ b/tests/workflows/preprocessing.nf.test
@@ -49,6 +49,12 @@ nextflow_workflow {
                 ]
                 // genelists
                 input[2] = null
+                // mqc_config
+                input[3] = [file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true)]
+                // mqc_logo
+                input[4] = []
+                // mqc methods description
+                input[5] = null
                 """
             }
         }
@@ -57,12 +63,12 @@ nextflow_workflow {
             assert workflow.success
             assert snapshot(
                 sanitizeOutput(workflow.out, unstableKeys:[
-                    "multiqc_main_report",
-                    "multiqc_main_data",
-                    "multiqc_main_plots",
-                    "multiqc_library_report",
-                    "multiqc_library_data",
-                    "multiqc_library_plots",
+                    "multiqc_report",
+                    "multiqc_data",
+                    "multiqc_plots",
+                    "multiqcsav_report",
+                    "multiqcsav_data",
+                    "multiqcsav_plots",
                     "md5sums",
                     "fastp_html",
                     "crams",
@@ -119,6 +125,12 @@ nextflow_workflow {
                 ]
                 // genelists
                 input[2] = null
+                // mqc_config
+                input[3] = [file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true)]
+                // mqc_logo
+                input[4] = []
+                // mqc methods description
+                input[5] = null
                 """
             }
         }
@@ -127,12 +139,12 @@ nextflow_workflow {
             assert workflow.success
             assert snapshot(
                 sanitizeOutput(workflow.out, unstableKeys:[
-                    "multiqc_main_report",
-                    "multiqc_main_data",
-                    "multiqc_main_plots",
-                    "multiqc_library_report",
-                    "multiqc_library_data",
-                    "multiqc_library_plots",
+                    "multiqc_report",
+                    "multiqc_data",
+                    "multiqc_plots",
+                    "multiqcsav_report",
+                    "multiqcsav_data",
+                    "multiqcsav_plots",
                     "md5sums",
                     "fastp_html",
                     "crams",
@@ -194,6 +206,12 @@ nextflow_workflow {
                 ]
                 // genelists
                 input[2] = null
+                // mqc_config
+                input[3] = [file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true)]
+                // mqc_logo
+                input[4] = []
+                // mqc methods description
+                input[5] = null
                 """
             }
         }
@@ -202,12 +220,12 @@ nextflow_workflow {
             assert workflow.success
             assert snapshot(
                 sanitizeOutput(workflow.out, unstableKeys:[
-                    "multiqc_main_report",
-                    "multiqc_main_data",
-                    "multiqc_main_plots",
-                    "multiqc_library_report",
-                    "multiqc_library_data",
-                    "multiqc_library_plots",
+                    "multiqc_report",
+                    "multiqc_data",
+                    "multiqc_plots",
+                    "multiqcsav_report",
+                    "multiqcsav_data",
+                    "multiqcsav_plots",
                     "md5sums",
                     "fastp_html",
                     "crams",
@@ -222,4 +240,79 @@ nextflow_workflow {
             ).match()
         }
     }
+
+    test("preprocessing - flowcell - bwa - bamsormadup - roi") {
+        when {
+            workflow {
+                """
+                // ch_samplesheet
+                input[0] = Channel.of(
+                    [
+                        [                           // meta
+                            id: "200624_A00834_0183_BHMTFYDRXX",
+                            lane: 1,
+                        ],
+                        [],  // fastq_1
+                        [],  // fastq_2
+                        file("https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleSheet_2.csv", checkIfExists: true),
+                        file("https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleInfo_2.json", checkIfExists: true),
+                        file("s3://test-data/genomics/homo_sapiens/illumina/bcl", checkIfExists: true)
+
+                    ]
+                )
+                // genomes
+                input[1] = [
+                    GRCh38: [
+                        fasta: "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.fna",
+                        fai: "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.fna.fai",
+                        dict: "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.dict",
+                        bwamem: "s3://test-data/genomics/homo_sapiens/genome/bwa/",
+                        gtf: "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.gtf"
+                    ]
+                ]
+                // genelists
+                input[2] = null
+                // mqc_config
+                input[3] = [file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true)]
+                // mqc_logo
+                input[4] = []
+                // mqc methods description
+                input[5] = null
+                """
+            }
+        }
+
+        then {
+            assert workflow.success
+            assert snapshot(
+                sanitizeOutput(workflow.out, unstableKeys:[
+                    "demultiplex_logs",
+                    "demultiplex_reports",
+                    "multiqc_report",
+                    "multiqc_data",
+                    "multiqc_plots",
+                    "multiqcsav_report",
+                    "multiqcsav_data",
+                    "multiqcsav_plots",
+                    "md5sums",
+                    "fastp_html",
+                    "crams",
+                    "picard_wgsmetrics",
+                    "picard_multiplemetrics_pdf",
+                    "picard_multiplemetrics",
+                    "picard_hsmetrics",
+                    "samtools_flagstat",
+                    "samtools_coverage",
+                    "samtools_stats"
+                ]).collectEntries { key, value ->
+                    if (key in ["demultiplex_logs", "demultiplex_reports"]) {
+                        [ key: value.sort() ]
+                    } else {
+                        [ key: value ]
+                    }
+                }
+            ).match()
+        }
+    }
+
 }
diff --git a/tests/workflows/preprocessing.nf.test.snap b/tests/workflows/preprocessing.nf.test.snap
index e6421b43..f1d096fa 100644
--- a/tests/workflows/preprocessing.nf.test.snap
+++ b/tests/workflows/preprocessing.nf.test.snap
@@ -71,7 +71,7 @@
                             "markdup": "bamsormadup",
                             "organism": "Homo sapiens",
                             "readgroup": {
-                                "CN": "CMGG",
+                                "CN": "",
                                 "ID": "H5T2YDSX3.1",
                                 "LB": "test",
                                 "PL": "ILLUMINA",
@@ -103,12 +103,12 @@
                             "roi": "/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/regions/roi_chr21.bed",
                             "single_end": false,
                             "readgroup": {
-                                "CN": "CMGG",
-                                "ID": "H5T2YDSX3.1",
-                                "PU": "H5T2YDSX3.1",
+                                "LB": "test",
+                                "CN": "",
                                 "PL": "ILLUMINA",
                                 "SM": "sample1",
-                                "LB": "test"
+                                "ID": "H5T2YDSX3.1",
+                                "PU": "H5T2YDSX3.1"
                             },
                             "genome": "GRCh38",
                             "genome_data": {
@@ -422,28 +422,38 @@
                 "mosdepth_thresholds_csi": [
                     
                 ],
-                "multiqc_library_data": [
-                    "test_data"
+                "multiqc_data": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        "test_data"
+                    ]
                 ],
-                "multiqc_library_plots": [
+                "multiqc_plots": [
                     
                 ],
-                "multiqc_library_report": [
-                    "test.html"
+                "multiqc_report": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        "test.html"
+                    ]
                 ],
-                "multiqc_main_data": [
+                "multiqcsav_data": [
                     [
-                        "multiqc_data"
+                        
                     ]
                 ],
-                "multiqc_main_plots": [
+                "multiqcsav_plots": [
                     [
                         
                     ]
                 ],
-                "multiqc_main_report": [
+                "multiqcsav_report": [
                     [
-                        "multiqc.html"
+                        
                     ]
                 ],
                 "panelcoverage": [
@@ -706,7 +716,7 @@
             "nf-test": "0.9.3",
             "nextflow": "25.10.4"
         },
-        "timestamp": "2026-02-11T21:21:37.068608"
+        "timestamp": "2026-03-05T14:04:31.848677"
     },
     "preprocessing - fastq - bwa - bamsormadup - roi - no coverage/no picard": {
         "content": [
@@ -782,7 +792,7 @@
                             "markdup": "bamsormadup",
                             "organism": "Homo sapiens",
                             "readgroup": {
-                                "CN": "CMGG",
+                                "CN": "",
                                 "ID": "H5T2YDSX3.1",
                                 "LB": "test",
                                 "PL": "ILLUMINA",
@@ -815,12 +825,12 @@
                             "roi": "/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/regions/roi_chr21.bed",
                             "single_end": false,
                             "readgroup": {
-                                "CN": "CMGG",
-                                "ID": "H5T2YDSX3.1",
-                                "PU": "H5T2YDSX3.1",
+                                "LB": "test",
+                                "CN": "",
                                 "PL": "ILLUMINA",
                                 "SM": "sample1",
-                                "LB": "test"
+                                "ID": "H5T2YDSX3.1",
+                                "PU": "H5T2YDSX3.1"
                             },
                             "genome": "GRCh38",
                             "genome_data": {
@@ -901,28 +911,38 @@
                 "mosdepth_thresholds_csi": [
                     
                 ],
-                "multiqc_library_data": [
-                    "test_data"
+                "multiqc_data": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        "test_data"
+                    ]
                 ],
-                "multiqc_library_plots": [
+                "multiqc_plots": [
                     
                 ],
-                "multiqc_library_report": [
-                    "test.html"
+                "multiqc_report": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        "test.html"
+                    ]
                 ],
-                "multiqc_main_data": [
+                "multiqcsav_data": [
                     [
-                        "multiqc_data"
+                        
                     ]
                 ],
-                "multiqc_main_plots": [
+                "multiqcsav_plots": [
                     [
                         
                     ]
                 ],
-                "multiqc_main_report": [
+                "multiqcsav_report": [
                     [
-                        "multiqc.html"
+                        
                     ]
                 ],
                 "panelcoverage": [
@@ -1075,7 +1095,7 @@
             "nf-test": "0.9.3",
             "nextflow": "25.10.4"
         },
-        "timestamp": "2026-02-11T21:25:53.804729"
+        "timestamp": "2026-03-05T14:08:24.880945"
     },
     "preprocessing - fastq - bwa - bamsormadup - no roi": {
         "content": [
@@ -1148,7 +1168,7 @@
                             "markdup": "bamsormadup",
                             "organism": "Homo sapiens",
                             "readgroup": {
-                                "CN": "CMGG",
+                                "CN": "",
                                 "ID": "H5T2YDSX3.1",
                                 "LB": "test",
                                 "PL": "ILLUMINA",
@@ -1178,12 +1198,12 @@
                             "run_coverage": true,
                             "single_end": false,
                             "readgroup": {
-                                "CN": "CMGG",
-                                "ID": "H5T2YDSX3.1",
-                                "PU": "H5T2YDSX3.1",
+                                "LB": "test",
+                                "CN": "",
                                 "PL": "ILLUMINA",
                                 "SM": "sample1",
-                                "LB": "test"
+                                "ID": "H5T2YDSX3.1",
+                                "PU": "H5T2YDSX3.1"
                             },
                             "genome": "GRCh38",
                             "genome_data": {
@@ -1412,28 +1432,38 @@
                 "mosdepth_thresholds_csi": [
                     
                 ],
-                "multiqc_library_data": [
-                    "test_data"
+                "multiqc_data": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        "test_data"
+                    ]
                 ],
-                "multiqc_library_plots": [
+                "multiqc_plots": [
                     
                 ],
-                "multiqc_library_report": [
-                    "test.html"
+                "multiqc_report": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        "test.html"
+                    ]
                 ],
-                "multiqc_main_data": [
+                "multiqcsav_data": [
                     [
-                        "multiqc_data"
+                        
                     ]
                 ],
-                "multiqc_main_plots": [
+                "multiqcsav_plots": [
                     [
                         
                     ]
                 ],
-                "multiqc_main_report": [
+                "multiqcsav_report": [
                     [
-                        "multiqc.html"
+                        
                     ]
                 ],
                 "panelcoverage": [
@@ -1685,9 +1715,68 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.4",
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-05T14:07:05.010944"
+    },
+    "preprocessing - flowcell - bwa - bamsormadup - roi": {
+        "content": [
+            {
+                "key": [
+                    [
+                        {
+                            "groupSize": 1,
+                            "groupTarget": {
+                                "single_end": true,
+                                "samplename": "Sample1",
+                                "sample_type": "DNA",
+                                "library": "test",
+                                "tag": "WES",
+                                "purpose": [
+                                    
+                                ],
+                                "organism": "Homo sapiens",
+                                "genome": "GRCh38",
+                                "vivar_project": [
+                                    
+                                ],
+                                "binsize": [
+                                    
+                                ],
+                                "panels": [
+                                    
+                                ],
+                                "aligner": "bwamem",
+                                "markdup": "bamsormadup",
+                                "umi_aware": false,
+                                "skip_trimming": false,
+                                "trim_front": 0,
+                                "trim_tail": 0,
+                                "adapter_R1": null,
+                                "adapter_R2": null,
+                                "run_coverage": true,
+                                "disable_picard_metrics": true,
+                                "roi": null,
+                                "genome_data": {
+                                    "fasta": "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.fna",
+                                    "fai": "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.fna.fai",
+                                    "dict": "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.dict",
+                                    "bwamem": "s3://test-data/genomics/homo_sapiens/genome/bwa/",
+                                    "gtf": "s3://test-data/genomics/homo_sapiens/genome/seq/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set_chr21.gtf"
+                                },
+                                "id": "Sample1"
+                            }
+                        },
+                        "Sample1.merged.metrics.txt:md5,f23933cc5957694d2286bdae22039097"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
             "nextflow": "25.10.4"
         },
-        "timestamp": "2026-02-18T15:49:18.459258"
+        "timestamp": "2026-03-05T14:02:04.847019"
     }
 }
\ No newline at end of file
diff --git a/workflows/preprocessing.nf b/workflows/preprocessing.nf
index 225c1ba4..d654dfd5 100644
--- a/workflows/preprocessing.nf
+++ b/workflows/preprocessing.nf
@@ -1,4 +1,4 @@
-include { samplesheetToList          } from 'plugin/nf-schema'
+include { samplesheetToList           } from 'plugin/nf-schema'
 
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -7,26 +7,27 @@ include { samplesheetToList          } from 'plugin/nf-schema'
 */
 
 // Modules
-include { BCLCONVERT                 } from '../modules/nf-core/bclconvert/main'
-include { FALCO                      } from '../modules/nf-core/falco/main'
-include { FASTP                      } from '../modules/nf-core/fastp/main'
-include { MD5SUM                     } from '../modules/nf-core/md5sum/main'
-include { MULTIQC as MULTIQC_LIBRARY } from '../modules/nf-core/multiqc/main'
-include { MULTIQC as MULTIQC_MAIN    } from '../modules/nf-core/multiqc/main'
-include { SAMTOOLS_COVERAGE          } from '../modules/nf-core/samtools/coverage/main'
+include { BCLCONVERT                  } from '../modules/nf-core/bclconvert'
+include { FALCO                       } from '../modules/nf-core/falco'
+include { FASTP                       } from '../modules/nf-core/fastp'
+include { MD5SUM                      } from '../modules/nf-core/md5sum'
+include { MULTIQC                     } from '../modules/nf-core/multiqc'
+include { MULTIQCSAV                  } from '../modules/nf-core/multiqcsav'
+include { SAMTOOLS_COVERAGE           } from '../modules/nf-core/samtools/coverage'
 
 // Subworkflows
-include { BAM_QC                     } from '../subworkflows/local/bam_qc/main'
-include { COVERAGE                   } from '../subworkflows/local/coverage/main'
-include { FASTQ_TO_CRAM              } from '../subworkflows/local/fastq_to_aligned_cram/main'
+include { BAM_QC                      } from '../subworkflows/local/bam_qc'
+include { COVERAGE                    } from '../subworkflows/local/coverage'
+include { FASTQ_TO_CRAM               } from '../subworkflows/local/fastq_to_aligned_cram'
 
 // Functions
-include { generateReadgroup          } from '../modules/nf-core/bclconvert/main'
-include { paramsSummaryMap           } from 'plugin/nf-schema'
-include { paramsSummaryMultiqc       } from '../subworkflows/nf-core/utils_nfcore_pipeline'
-include { softwareVersionsToYAML     } from '../subworkflows/nf-core/utils_nfcore_pipeline'
-include { methodsDescriptionText     } from '../subworkflows/local/utils_nfcore_preprocessing_pipeline'
-include { getGenomeAttribute         } from '../subworkflows/local/utils_nfcore_preprocessing_pipeline'
+include { getReadgroupsFromBclconvert } from '../subworkflows/local/utils_nfcmgg_preprocessing_pipeline'
+include { getReadgroupFromFastq       } from '../subworkflows/local/utils_nfcmgg_preprocessing_pipeline'
+include { paramsSummaryMap            } from 'plugin/nf-schema'
+include { paramsSummaryMultiqc        } from '../subworkflows/nf-core/utils_nfcore_pipeline'
+include { softwareVersionsToYAML      } from '../subworkflows/nf-core/utils_nfcore_pipeline'
+include { methodsDescriptionText      } from '../subworkflows/local/utils_nfcore_preprocessing_pipeline'
+include { getGenomeAttribute          } from '../subworkflows/local/utils_nfcore_preprocessing_pipeline'
 
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -39,6 +40,9 @@ workflow PREPROCESSING {
     ch_samplesheet // channel: samplesheet read in from --input
     genomes // map: genome reference files
     genelists // file: directory containing genelist bed files for coverage analysis
+    multiqc_config // file(s): MultiQC config file(s)
+    multiqc_logo // file: MultiQC logo file
+    multiqc_methods_description // file: custom methods description for MultiQC report
 
     main:
     ch_multiqc_files = channel.empty()
@@ -46,7 +50,7 @@ workflow PREPROCESSING {
     ch_samplesheet
         .branch { meta, fastq_1, fastq_2, samplesheet, sampleinfo, flowcell ->
             illumina_flowcell: (flowcell && samplesheet && sampleinfo) && !(fastq_1 || fastq_2)
-            return [meta, samplesheet, sampleinfo, flowcell]
+            return [["id": meta.id, "lane": meta.lane], samplesheet, sampleinfo, flowcell]
             fastq: (fastq_1) && !(flowcell || samplesheet || sampleinfo)
             return [meta, [fastq_1, fastq_2].findAll()]
             other: true
@@ -72,34 +76,50 @@ workflow PREPROCESSING {
     // BCLCONVERT([meta, samplesheet, flowcell])
     BCLCONVERT(ch_illumina_flowcell.flowcell)
     BCLCONVERT.out.fastq.dump(tag: "DEMULTIPLEX: fastq", pretty: true)
-    ch_multiqc_files = ch_multiqc_files.mix(
-        BCLCONVERT.out.reports,
-        BCLCONVERT.out.logs,
-    )
 
-    generateReadgroup(
+    getReadgroupsFromBclconvert(
         BCLCONVERT.out.reports.map { meta, reports ->
-            return [meta, reports.find { report -> report.name == "fastq_list.csv" }]
+            return [meta, file(reports).resolve("fastq_list.csv")]
         },
         BCLCONVERT.out.fastq,
-    ).set { ch_fastq_with_meta }
-    ch_fastq_with_meta.dump(tag: "DEMULTIPLEX: fastq with meta", pretty: true)
+    ).dump(tag: "DEMULTIPLEX: fastq with meta", pretty: true).map { meta, fastq -> [meta.readgroup.SM, meta, fastq] }.set { ch_demultiplexed_fastq }
+
+    // Run QC
+    ch_mqcsav_input = ch_illumina_flowcell.flowcell
+        .map { meta, _samplesheet, flowcell ->
+            def interop = files(flowcell.resolve("InterOp/*.bin"), checkIfExists: true)
+            def xml = files(flowcell.resolve("*.xml"), checkIfExists: true)
+            return [meta, xml, interop]
+        }
+        .join(BCLCONVERT.out.reports, by: 0)
+        .map { meta, xml, interop, reports ->
+            return [meta - meta.subMap(['lane']), xml, interop, reports]
+        }
+        .groupTuple(by: [0])
+        .map { meta, xml, interop, reports ->
+            return [meta, xml.flatten().unique(), interop.flatten().unique(), reports.flatten(), multiqc_config, multiqc_logo, [], []]
+        }
+        .dump(tag: "MULTIQC SAV input", pretty: true)
 
-    ch_fastq_with_meta.map { meta, fastq -> [meta.readgroup.SM, meta, fastq] }.set { ch_demultiplexed_fastq }
+    MULTIQCSAV(
+        ch_mqcsav_input
+    )
 
+    // Merge fastq meta with sample info
     ch_illumina_flowcell.info
         .flatten()
         .transpose()
         .map { sampleinfo -> [sampleinfo.samplename, sampleinfo] }
         .set { ch_sampleinfo }
 
-    // Merge fastq meta with sample info
     ch_demultiplexed_fastq
         .combine(ch_sampleinfo, by: 0)
         .map { _samplename, meta, fastq, sampleinfo ->
+            def new_rg = [:]
             if (sampleinfo.library) {
                 new_rg = meta.readgroup + ['LB': sampleinfo.library]
-            } else {
+            }
+            else {
                 new_rg = meta.readgroup
             }
             def new_meta = meta + sampleinfo + ['readgroup': new_rg]
@@ -114,6 +134,7 @@ workflow PREPROCESSING {
             other: true
         }
         .set { ch_demultiplexed_fastq_with_sampleinfo }
+
     /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 // PROCESS FASTQ INPUTS
@@ -125,15 +146,9 @@ workflow PREPROCESSING {
             // if no fastq_2, then single-end
             def single_end = fastq[1] ? false : true
             // add readgroup metadata
-            def rg = readgroup_from_fastq(fastq[0])
             // if the sample name starts with "snp_", remove it so the sampletracking works later on.
             def samplename = meta.samplename.startsWith("snp_") ? meta.samplename.substring(4) : meta.samplename
-            rg = rg + [
-                'SM': samplename,
-                'LB': meta.library ?: "",
-                'PL': meta.platform ?: rg.PL,
-                'ID': meta.readgroup ?: rg.ID,
-            ]
+            def rg = getReadgroupFromFastq(fastq[0], samplename, meta.library, meta.platform)
             def meta_with_readgroup = meta + ['single_end': single_end, 'readgroup': rg]
             return [meta_with_readgroup, fastq]
         }
@@ -363,67 +378,50 @@ workflow PREPROCESSING {
             sort: true,
             newLine: true,
         )
-        .map { file -> [[id: 'main'], file] }
         .set { ch_collated_versions }
 
     //
     // MODULE: MultiQC
     //
-    ch_multiqc_config = channel.fromPath("${projectDir}/assets/multiqc_config.yml", checkIfExists: true)
-    ch_multiqc_custom_config = params.multiqc_config ? channel.fromPath(params.multiqc_config, checkIfExists: true) : channel.empty()
-    ch_multiqc_logo = params.multiqc_logo ? channel.fromPath(params.multiqc_logo, checkIfExists: true) : channel.empty()
-
+    // summary files without meta, e.g. versions, params
     summary_params = paramsSummaryMap(workflow, parameters_schema: "nextflow_schema.json")
     ch_workflow_summary = channel.value(paramsSummaryMultiqc(summary_params))
-    ch_multiqc_files = ch_multiqc_files.mix(ch_workflow_summary.collectFile(name: 'workflow_summary_mqc.yaml').map { file -> [[id: 'main'], file] })
+    ch_methods_description = channel.value(multiqc_methods_description ? methodsDescriptionText(multiqc_methods_description) : "")
 
-    ch_multiqc_custom_methods_description = params.multiqc_methods_description ? file(params.multiqc_methods_description, checkIfExists: true) : file("${projectDir}/assets/methods_description_template.yml", checkIfExists: true)
-    ch_methods_description = channel.value(methodsDescriptionText(ch_multiqc_custom_methods_description))
+    ch_summary_files = channel.empty()
+        .mix(ch_workflow_summary.collectFile(name: 'workflow_summary_mqc.yaml'))
+        .mix(ch_collated_versions)
+        .mix(ch_methods_description.collectFile(name: 'methods_description_mqc.yaml', sort: true))
+        .toList()
+        .map { files -> [files] }
+        .dump(tag: "Summary files for MultiQC", pretty: true)
 
-    ch_multiqc_files = ch_multiqc_files.mix(ch_collated_versions)
-    ch_multiqc_files = ch_multiqc_files.mix(ch_methods_description.collectFile(name: 'methods_description_mqc.yaml', sort: true).map { file -> [[id: 'main'], file] })
-
-    ch_multiqc_files = ch_multiqc_files
+    ch_multiqc_input = ch_multiqc_files
         .map { meta, files ->
-            return [meta.library ? [id: meta.library] : [id: 'main'], files]
+            def new_meta = meta.library ? [id: meta.library] : [id: 'multiqc']
+            return [new_meta, files]
         }
-        .branch { meta, files ->
-            main: meta.id == 'main'
-            return files
-            library: meta.id != 'main'
-            return [meta, files instanceof List ? files : [files]]
+        .groupTuple(by: 0)
+        .combine(ch_summary_files)
+        .map { meta, multiqc_files, summary_files ->
+            return [meta, (multiqc_files + summary_files).flatten(), multiqc_config.flatten(), multiqc_logo, [], []]
         }
+        .dump(tag: "MULTIQC files", pretty: true)
 
 
-
-    ch_library_multiqc_files = ch_multiqc_files.library.transpose(by: 1).groupTuple()
-    ch_library_multiqc_files.dump(tag: "MULTIQC files - library", pretty: true)
-
-
-    ch_main_multiqc_files = ch_multiqc_files.main.collect().map { files -> [[id: 'main'], files] }
-    ch_main_multiqc_files.dump(tag: "MULTIQC files - main", pretty: true)
-    MULTIQC_MAIN(
-        ch_main_multiqc_files,
-        ch_multiqc_config.toList(),
-        ch_multiqc_custom_config.toList(),
-        ch_multiqc_logo.toList(),
-        [],
-        [],
-    )
-
-    MULTIQC_LIBRARY(
-        ch_library_multiqc_files,
-        ch_multiqc_config.toList(),
-        ch_multiqc_custom_config.toList(),
-        ch_multiqc_logo.toList(),
-        [],
-        [],
-    )
+    // MULTIQC([meta, multiqc_files, multiqc_config, multiqc_logo, replace_names, sample_names])
+    MULTIQC(ch_multiqc_input)
 
     emit:
-    demultiplex_interop        = BCLCONVERT.out.interop
-    demultiplex_reports        = BCLCONVERT.out.reports
-    demultiplex_logs           = BCLCONVERT.out.logs
+    demultiplex_reports        = BCLCONVERT.out.reports.map { meta, reports ->
+        return [meta, files(reports.resolve("*"))]
+    }
+    demultiplex_logs           = BCLCONVERT.out.logs.map { meta, logs ->
+        return [meta, files(logs.resolve("*"))]
+    }
+    demultiplex_interop        = ch_illumina_flowcell.flowcell.map { meta, _samplesheet, flowcell ->
+        return [meta, files(flowcell.resolve("InterOp/*.bin"))]
+    }
     demultiplex_fastq          = ch_demultiplexed_fastq_with_sampleinfo.other
     falco_html                 = FALCO.out.html
     falco_txt                  = FALCO.out.txt
@@ -456,56 +454,10 @@ workflow PREPROCESSING {
     picard_wgsmetrics          = BAM_QC.out.picard_wgsmetrics
     picard_hsmetrics           = BAM_QC.out.picard_hsmetrics
     md5sums                    = MD5SUM.out.checksum
-    multiqc_main_report        = MULTIQC_MAIN.out.report.toList()
-    multiqc_main_data          = MULTIQC_MAIN.out.data.toList()
-    multiqc_main_plots         = MULTIQC_MAIN.out.plots.toList()
-    multiqc_library_report     = MULTIQC_LIBRARY.out.report
-    multiqc_library_data       = MULTIQC_LIBRARY.out.data
-    multiqc_library_plots      = MULTIQC_LIBRARY.out.plots
-}
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    FUNCTIONS
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
-// https://github.com/nf-core/sarek/blob/7ba61bde8e4f3b1932118993c766ed33b5da465e/workflows/sarek.nf#L1014-L1040
-def readgroup_from_fastq(path) {
-    // expected format:
-    // xx:yy:FLOWCELLID:LANE:... (seven fields)
-    // or
-    // FLOWCELLID:LANE:xx:... (five fields)
-    def line
-
-    path.withInputStream { fq ->
-        def gzipStream = new java.util.zip.GZIPInputStream(fq) as InputStream
-        def decoder = new InputStreamReader(gzipStream, 'ASCII')
-        def buffered = new BufferedReader(decoder)
-        line = buffered.readLine()
-    }
-    assert line.startsWith('@')
-    line = line.substring(1)
-    def fields = line.split(':')
-    def rg = [:]
-    rg.CN = "CMGG"
-
-    if (fields.size() >= 7) {
-        // CASAVA 1.8+ format, from  https://support.illumina.com/help/BaseSpace_OLH_009008/Content/Source/Informatics/BS/FileFormat_FASTQ-files_swBS.htm
-        // "@<instrument>:<run number>:<flowcell ID>:<lane>:<tile>:<x-pos>:<y-pos>:<UMI> <read>:<is filtered>:<control number>:<index>"
-        // def sequencer_serial = fields[0]
-        // def run_number       = fields[1]
-        def fcid = fields[2]
-        def lane = fields[3]
-        def index = fields[-1] =~ /[GATC+-]/ ? fields[-1] : ""
-
-        rg.ID = [index ?: fcid, lane].join(".")
-        rg.PU = [fcid, lane].join(".")
-        rg.PL = "ILLUMINA"
-    }
-    else if (fields.size() == 5) {
-        def fcid = fields[0]
-        rg.ID = fcid
-    }
-    return rg
+    multiqcsav_report          = MULTIQCSAV.out.report.toList()
+    multiqcsav_data            = MULTIQCSAV.out.data.toList()
+    multiqcsav_plots           = MULTIQCSAV.out.plots.toList()
+    multiqc_report             = MULTIQC.out.report
+    multiqc_data               = MULTIQC.out.data
+    multiqc_plots              = MULTIQC.out.plots
 }

From 04252195bbaf2f1088c4e94664c279e8a57b869c Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 5 Mar 2026 14:39:11 +0100
Subject: [PATCH 14/24] restore InterOp output

---
 main.nf                    | 6 ++++--
 workflows/preprocessing.nf | 4 +---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/main.nf b/main.nf
index e2f78c8b..b98a9eeb 100644
--- a/main.nf
+++ b/main.nf
@@ -70,7 +70,7 @@ workflow {
     demultiplex_reports        = PREPROCESSING.out.demultiplex_reports.transpose()
     demultiplex_logs           = PREPROCESSING.out.demultiplex_logs.transpose()
     demultiplex_fastq          = PREPROCESSING.out.demultiplex_fastq.transpose()
-    demultiplex_interop        = PREPROCESSING.out.demultiplex_interop.transpose()
+    demultiplex_interop        = PREPROCESSING.out.demultiplex_interop.transpose(by: 1)
     falco_html                 = PREPROCESSING.out.falco_html
     falco_txt                  = PREPROCESSING.out.falco_txt
     fastp_json                 = PREPROCESSING.out.fastp_json
@@ -122,7 +122,9 @@ output {
         }
     }
     demultiplex_interop {
-        path "InterOp/"
+        path { _meta, bin ->
+            bin >> "Interop/${bin.name}"
+        }
     }
     demultiplex_fastq {
         path { meta, fastq ->
diff --git a/workflows/preprocessing.nf b/workflows/preprocessing.nf
index d654dfd5..5337ae93 100644
--- a/workflows/preprocessing.nf
+++ b/workflows/preprocessing.nf
@@ -419,9 +419,7 @@ workflow PREPROCESSING {
     demultiplex_logs           = BCLCONVERT.out.logs.map { meta, logs ->
         return [meta, files(logs.resolve("*"))]
     }
-    demultiplex_interop        = ch_illumina_flowcell.flowcell.map { meta, _samplesheet, flowcell ->
-        return [meta, files(flowcell.resolve("InterOp/*.bin"))]
-    }
+    demultiplex_interop        = BCLCONVERT.out.interop
     demultiplex_fastq          = ch_demultiplexed_fastq_with_sampleinfo.other
     falco_html                 = FALCO.out.html
     falco_txt                  = FALCO.out.txt

From ffacaf730e6ac982ea58b27aaa364c6180bff229 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 5 Mar 2026 19:50:18 +0100
Subject: [PATCH 15/24] Bump modules (#161)

* bump modules

* update subwf, include nf-teams

* fix errors
---
 assets/adaptivecard.json                      | 67 -------------------
 assets/slackreport.json                       | 34 ----------
 main.nf                                       |  1 -
 modules.json                                  |  4 +-
 modules/nf-core/samtools/coverage/main.nf     |  7 +-
 .../samtools/coverage/tests/main.nf.test.snap | 18 ++---
 nextflow.config                               | 10 +++
 .../main.nf                                   |  5 --
 .../nf-core/utils_nfcore_pipeline/main.nf     | 64 ------------------
 .../{main.workflow.nf.test => main.nf.test}   |  0
 ...orkflow.nf.test.snap => main.nf.test.snap} |  0
 11 files changed, 22 insertions(+), 188 deletions(-)
 delete mode 100644 assets/adaptivecard.json
 delete mode 100644 assets/slackreport.json
 rename subworkflows/nf-core/utils_nfcore_pipeline/tests/{main.workflow.nf.test => main.nf.test} (100%)
 rename subworkflows/nf-core/utils_nfcore_pipeline/tests/{main.workflow.nf.test.snap => main.nf.test.snap} (100%)

diff --git a/assets/adaptivecard.json b/assets/adaptivecard.json
deleted file mode 100644
index 92ed5f06..00000000
--- a/assets/adaptivecard.json
+++ /dev/null
@@ -1,67 +0,0 @@
-{
-    "type": "message",
-    "attachments": [
-        {
-            "contentType": "application/vnd.microsoft.card.adaptive",
-            "contentUrl": null,
-            "content": {
-                "\$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
-                "msteams": {
-                    "width": "Full"
-                },
-                "type": "AdaptiveCard",
-                "version": "1.2",
-                "body": [
-                    {
-                        "type": "TextBlock",
-                        "size": "Large",
-                        "weight": "Bolder",
-                        "color": "<% if (success) { %>Good<% } else { %>Attention<%} %>",
-                        "text": "nf-cmgg/preprocessing v${version} - ${runName}",
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "spacing": "None",
-                        "text": "Completed at ${dateComplete} (duration: ${duration})",
-                        "isSubtle": true,
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "text": "<% if (success) { %>Pipeline completed successfully!<% } else { %>Pipeline completed with errors. The full error message was: ${errorReport}.<% } %>",
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "text": "The command used to launch the workflow was as follows:",
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "text": "${commandLine}",
-                        "isSubtle": true,
-                        "wrap": true
-                    }
-                ],
-                "actions": [
-                    {
-                        "type": "Action.ShowCard",
-                        "title": "Pipeline Configuration",
-                        "card": {
-                            "type": "AdaptiveCard",
-                            "\$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
-                            "body": [
-                                {
-                                    "type": "FactSet",
-                                    "facts": [<% out << summary.collect{ k,v -> "{\"title\": \"$k\", \"value\" : \"$v\"}"}.join(",\n") %>
-                                    ]
-                                }
-                            ]
-                        }
-                    }
-                ]
-            }
-        }
-    ]
-}
diff --git a/assets/slackreport.json b/assets/slackreport.json
deleted file mode 100644
index 84fc115a..00000000
--- a/assets/slackreport.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
-    "attachments": [
-        {
-            "fallback": "Plain-text summary of the attachment.",
-            "color": "<% if (success) { %>good<% } else { %>danger<%} %>",
-            "author_name": "nf-cmgg/preprocessing ${version} - ${runName}",
-            "author_icon": "https://www.nextflow.io/docs/latest/_static/favicon.ico",
-            "text": "<% if (success) { %>Pipeline completed successfully!<% } else { %>Pipeline completed with errors<% } %>",
-            "fields": [
-                {
-                    "title": "Command used to launch the workflow",
-                    "value": "```${commandLine}```",
-                    "short": false
-                }
-                <%
-                    if (!success) { %>
-                    ,
-                    {
-                        "title": "Full error message",
-                        "value": "```${errorReport}```",
-                        "short": false
-                    },
-                    {
-                        "title": "Pipeline configuration",
-                        "value": "<% out << summary.collect{ k,v -> k == "hook_url" ? "_${k}_: (_hidden_)" : ( ( v.class.toString().contains('Path') || ( v.class.toString().contains('String') && v.contains('/') ) ) ? "_${k}_: `${v}`" : (v.class.toString().contains('DateTime') ? ("_${k}_: " + v.format(java.time.format.DateTimeFormatter.ofLocalizedDateTime(java.time.format.FormatStyle.MEDIUM))) : "_${k}_: ${v}") ) }.join(",\n") %>",
-                        "short": false
-                    }
-                    <% }
-                %>
-            ],
-            "footer": "Completed at <% out << dateComplete.format(java.time.format.DateTimeFormatter.ofLocalizedDateTime(java.time.format.FormatStyle.MEDIUM)) %> (duration: ${duration})"
-        }
-    ]
-}
diff --git a/main.nf b/main.nf
index b98a9eeb..64e8d3db 100644
--- a/main.nf
+++ b/main.nf
@@ -62,7 +62,6 @@ workflow {
         params.plaintext_email,
         params.outdir,
         params.monochrome_logs,
-        params.hook_url,
         PREPROCESSING.out.multiqc_report,
     )
 
diff --git a/modules.json b/modules.json
index 410a4a68..5f13e2ed 100644
--- a/modules.json
+++ b/modules.json
@@ -103,7 +103,7 @@
                     },
                     "samtools/coverage": {
                         "branch": "master",
-                        "git_sha": "b2e78932ef01165fd85829513eaca29eff8e640a",
+                        "git_sha": "440edf75d8782913115a7b72a88392a227f72cc1",
                         "installed_by": ["modules"],
                         "patch": "modules/nf-core/samtools/coverage/samtools-coverage.diff"
                     },
@@ -170,7 +170,7 @@
                     },
                     "utils_nfcore_pipeline": {
                         "branch": "master",
-                        "git_sha": "65f5e638d901a51534c68fd5c1c19e8112fb4df1",
+                        "git_sha": "f0b535b3ae20080f8db03dd5388876ad1ec29d70",
                         "installed_by": ["subworkflows"]
                     },
                     "utils_nfschema_plugin": {
diff --git a/modules/nf-core/samtools/coverage/main.nf b/modules/nf-core/samtools/coverage/main.nf
index 25754d35..ff566999 100644
--- a/modules/nf-core/samtools/coverage/main.nf
+++ b/modules/nf-core/samtools/coverage/main.nf
@@ -37,11 +37,6 @@ process SAMTOOLS_COVERAGE {
     stub:
     def prefix = task.ext.prefix ?: "${meta.id}"
     """
-    touch ${prefix}.txt
-
-    cat <<-END_VERSIONS > versions.yml
-    "${task.process}":
-        samtools: \$(echo \$(samtools --version 2>&1) | sed 's/^.*samtools //; s/Using.*\$//' )
-    END_VERSIONS
+    echo "#rname\tstartpos\tendpos\tnumreads\tcovbases\tcoverage\tmeandepth\tmeanbaseq\tmeanmapq" > ${prefix}.txt
     """
 }
diff --git a/modules/nf-core/samtools/coverage/tests/main.nf.test.snap b/modules/nf-core/samtools/coverage/tests/main.nf.test.snap
index 256c6507..0d72500a 100644
--- a/modules/nf-core/samtools/coverage/tests/main.nf.test.snap
+++ b/modules/nf-core/samtools/coverage/tests/main.nf.test.snap
@@ -7,7 +7,7 @@
                         {
                             "id": "test"
                         },
-                        "test.txt:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        "test.txt:md5,ea885d54c0223dff86e2e44f5bc08374"
                     ]
                 ],
                 "versions_samtools": [
@@ -19,11 +19,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-03-03T16:16:25.616671578",
         "meta": {
-            "nf-test": "0.9.3",
-            "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-10T15:27:27.16473081"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
     "test_samtools_coverage_bam": {
         "content": [
@@ -45,11 +45,11 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-10T15:27:06.759689511",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-10T15:27:06.759689511"
+        }
     },
     "test_samtools_coverage_cram": {
         "content": [
@@ -86,10 +86,10 @@
                 ]
             }
         ],
+        "timestamp": "2026-02-10T15:27:12.949845604",
         "meta": {
             "nf-test": "0.9.3",
             "nextflow": "25.10.3"
-        },
-        "timestamp": "2026-02-10T15:27:12.949845604"
+        }
     }
 }
\ No newline at end of file
diff --git a/nextflow.config b/nextflow.config
index c3e162e8..44d9698c 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -269,6 +269,7 @@ manifest {
 // Nextflow plugins
 plugins {
     id 'nf-schema@2.6.1'
+    id 'nf-teams@0.1.0'
 }
 
 validation {
@@ -276,6 +277,15 @@ validation {
     monochromeLogs      = params.monochrome_logs
 }
 
+// TODO remove this once minimal nf-version is 26.0X with strict syntax.
+teams {
+    enabled            = true
+    webHook {
+        url = params.hook_url ? params.hook_url : System.getenv('TEAMS_WEBHOOK_URL')
+    }
+    onComplete.enabled = true
+}
+
 // Load modules.config for DSL2 module specific options
 includeConfig 'conf/modules.config'
 
diff --git a/subworkflows/local/utils_nfcore_preprocessing_pipeline/main.nf b/subworkflows/local/utils_nfcore_preprocessing_pipeline/main.nf
index f269c502..d7b7103b 100644
--- a/subworkflows/local/utils_nfcore_preprocessing_pipeline/main.nf
+++ b/subworkflows/local/utils_nfcore_preprocessing_pipeline/main.nf
@@ -14,7 +14,6 @@ include { samplesheetToList       } from 'plugin/nf-schema'
 include { paramsHelp              } from 'plugin/nf-schema'
 include { completionEmail         } from '../../nf-core/utils_nfcore_pipeline'
 include { completionSummary       } from '../../nf-core/utils_nfcore_pipeline'
-include { imNotification          } from '../../nf-core/utils_nfcore_pipeline'
 include { UTILS_NFCORE_PIPELINE   } from '../../nf-core/utils_nfcore_pipeline'
 include { UTILS_NEXTFLOW_PIPELINE } from '../../nf-core/utils_nextflow_pipeline'
 
@@ -99,7 +98,6 @@ workflow PIPELINE_COMPLETION {
     plaintext_email // boolean: Send plain-text email instead of HTML
     outdir //    path: Path to output directory where results will be published
     monochrome_logs // boolean: Disable ANSI colour codes in log output
-    hook_url //  string: hook URL for notifications
     multiqc_report //  string: Path to MultiQC report
 
     main:
@@ -123,9 +121,6 @@ workflow PIPELINE_COMPLETION {
         }
 
         completionSummary(monochrome_logs)
-        if (hook_url) {
-            imNotification(summary_params, hook_url)
-        }
     }
 
     workflow.onError {
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/main.nf b/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
index 2f30e9a4..bf568a08 100644
--- a/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
+++ b/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
@@ -353,67 +353,3 @@ def completionSummary(monochrome_logs=true) {
         log.info("-${colors.purple}[${workflow.manifest.name}]${colors.red} Pipeline completed with errors${colors.reset}-")
     }
 }
-
-//
-// Construct and send a notification to a web server as JSON e.g. Microsoft Teams and Slack
-//
-def imNotification(summary_params, hook_url) {
-    def summary = [:]
-    summary_params
-        .keySet()
-        .sort()
-        .each { group ->
-            summary << summary_params[group]
-        }
-
-    def misc_fields = [:]
-    misc_fields['start']          = workflow.start
-    misc_fields['complete']       = workflow.complete
-    misc_fields['scriptfile']     = workflow.scriptFile
-    misc_fields['scriptid']       = workflow.scriptId
-    if (workflow.repository) {
-        misc_fields['repository'] = workflow.repository
-    }
-    if (workflow.commitId) {
-        misc_fields['commitid']   = workflow.commitId
-    }
-    if (workflow.revision) {
-        misc_fields['revision']   = workflow.revision
-    }
-    misc_fields['nxf_version']    = workflow.nextflow.version
-    misc_fields['nxf_build']      = workflow.nextflow.build
-    misc_fields['nxf_timestamp']  = workflow.nextflow.timestamp
-
-    def msg_fields = [:]
-    msg_fields['version']      = getWorkflowVersion()
-    msg_fields['runName']      = workflow.runName
-    msg_fields['success']      = workflow.success
-    msg_fields['dateComplete'] = workflow.complete
-    msg_fields['duration']     = workflow.duration
-    msg_fields['exitStatus']   = workflow.exitStatus
-    msg_fields['errorMessage'] = (workflow.errorMessage ?: 'None')
-    msg_fields['errorReport']  = (workflow.errorReport ?: 'None')
-    msg_fields['commandLine']  = workflow.commandLine.replaceFirst(/ +--hook_url +[^ ]+/, "")
-    msg_fields['projectDir']   = workflow.projectDir
-    msg_fields['summary']      = summary << misc_fields
-
-    // Render the JSON template
-    def engine       = new groovy.text.GStringTemplateEngine()
-    // Different JSON depending on the service provider
-    // Defaults to "Adaptive Cards" (https://adaptivecards.io), except Slack which has its own format
-    def json_path     = hook_url.contains("hooks.slack.com") ? "slackreport.json" : "adaptivecard.json"
-    def hf            = new File("${workflow.projectDir}/assets/${json_path}")
-    def json_template = engine.createTemplate(hf).make(msg_fields)
-    def json_message  = json_template.toString()
-
-    // POST
-    def post = new URL(hook_url).openConnection()
-    post.setRequestMethod("POST")
-    post.setDoOutput(true)
-    post.setRequestProperty("Content-Type", "application/json")
-    post.getOutputStream().write(json_message.getBytes("UTF-8"))
-    def postRC = post.getResponseCode()
-    if (!postRC.equals(200)) {
-        log.warn(post.getErrorStream().getText())
-    }
-}
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test b/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test
similarity index 100%
rename from subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test
rename to subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap b/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test.snap
similarity index 100%
rename from subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap
rename to subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test.snap

From c763f0cf701be34f9f2f95ae9a50a7e0cf52d951 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Thu, 5 Mar 2026 21:00:11 +0100
Subject: [PATCH 16/24] Update/docs (#162)

* fix download test

* Update parameters docs

* Update output docs

* drop conda profiles

* update usage docs
---
 .github/workflows/download_pipeline.yml |  4 +-
 docs/output.md                          | 11 ++--
 docs/parameters.md                      | 69 ++++++++++++-------------
 docs/usage.md                           | 20 ++++---
 nextflow.config                         | 20 -------
 5 files changed, 50 insertions(+), 74 deletions(-)

diff --git a/.github/workflows/download_pipeline.yml b/.github/workflows/download_pipeline.yml
index 6d94bcbf..d9970752 100644
--- a/.github/workflows/download_pipeline.yml
+++ b/.github/workflows/download_pipeline.yml
@@ -95,14 +95,14 @@ jobs:
         env:
           NXF_SINGULARITY_CACHEDIR: ./singularity_container_images
           NXF_SINGULARITY_HOME_MOUNT: true
-        run: nextflow run ./${{needs.configure.outputs.REPOTITLE_LOWERCASE }}/$( sed 's/\W/_/g' <<< ${{ needs.configure.outputs.REPO_BRANCH }}) -stub -profile test,singularity --outdir ./results
+        run: nextflow run ./${{needs.configure.outputs.REPOTITLE_LOWERCASE }}/$( sed 's/\W/_/g' <<< ${{ needs.configure.outputs.REPO_BRANCH }}) -stub -profile test,singularity,s3_ugent --outdir ./results
       - name: Run the downloaded pipeline (stub run not supported)
         id: run_pipeline
         if: ${{ steps.stub_run_pipeline.outcome == 'failure' }}
         env:
           NXF_SINGULARITY_CACHEDIR: ./singularity_container_images
           NXF_SINGULARITY_HOME_MOUNT: true
-        run: nextflow run ./${{ needs.configure.outputs.REPOTITLE_LOWERCASE }}/$( sed 's/\W/_/g' <<< ${{ needs.configure.outputs.REPO_BRANCH }}) -profile test,singularity --outdir ./results
+        run: nextflow run ./${{ needs.configure.outputs.REPOTITLE_LOWERCASE }}/$( sed 's/\W/_/g' <<< ${{ needs.configure.outputs.REPO_BRANCH }}) -profile test,singularity,s3_ugent --outdir ./results
 
       - name: Count the downloaded number of container images
         id: count_afterwards
diff --git a/docs/output.md b/docs/output.md
index e57580fc..ed562aa8 100644
--- a/docs/output.md
+++ b/docs/output.md
@@ -10,7 +10,7 @@ The directories listed below will be created in the results directory after the
 
 ### Sample information
 
-A separate directory will be created in the output directory for each sample containing all output files for that sample.
+A separate directory will be created in the output directory for each sample containing all output files for that sample. If a library name is given in the samplesheet, the sample directories will be nested within a library directory.
 
 <details markdown="1">
 <summary>Output files</summary>
@@ -44,6 +44,7 @@ A separate directory will be created in the output directory for each sample con
   - `SAMPLE.regions.bed.gz`: The regional BED file, showing how well covered the requested regions are, calculated by `mosdepth` (only when a ROI BED file has been given)
   - `SAMPLE.regions.bed.gz.csi`: The index of the regional BED file
   - `SAMPLE.stats`: General statistics for the sample
+  - `multiqc/`: Directory containing the MultiQC report for the sample or library
   </details>
 
 ### Extra outputs for flowcell inputs
@@ -70,9 +71,9 @@ Some additional files will be created when a flowcell input has been used.
 
 </details>
 
-[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.
+[MultiQC](https://seqera.io/multiqc/) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.
 
-Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>.
+Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <https://seqera.io/multiqc/>.
 
 ### Pipeline information
 
@@ -80,10 +81,8 @@ Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQ
 <summary>Output files</summary>
 
 - `pipeline_info/`
-  - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.dot`/`pipeline_dag.svg`.
+  - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.mmd`.
   - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameter's are used when running the pipeline.
-  - Reformatted samplesheet files used as input to the pipeline: `samplesheet.valid.csv`.
-  - Parameters used by the pipeline run: `params.json`.
 
 </details>
 
diff --git a/docs/parameters.md b/docs/parameters.md
index 18c594f1..700a2292 100644
--- a/docs/parameters.md
+++ b/docs/parameters.md
@@ -12,51 +12,50 @@ Define where the pipeline should find input data and save output data.
 | `outdir`        | The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure.                                                                                                                                                                                                                                    | `string` |         | True     |        |
 | `email`         | Email address for completion summary. <details><summary>Help</summary><small>Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (`~/.nextflow/config`) then you don't need to specify this on the command line for every run.</small></details> | ``       |         |          |        |
 | `multiqc_title` | MultiQC report title. Printed as page header, used for filename if not otherwise specified.                                                                                                                                                                                                                                                                 | `string` |         |          |        |
+| `genomes`       |                                                                                                                                                                                                                                                                                                                                                             | `object` |         |          | True   |
 
 ## Pipeline options
 
-| Parameter                | Description                                                             | Type      | Default     | Required | Hidden |
-| ------------------------ | ----------------------------------------------------------------------- | --------- | ----------- | -------- | ------ |
-| `markdup`                | Which alignment postprocessor to use                                    | `string`  | bamsormadup |          |        |
-| `run_coverage`           | Run coverage analysis steps                                             | `boolean` | True        |          |        |
-| `skip_trimming`          | Skip adapter trimming                                                   | `boolean` | False       |          |        |
-| `split_fastq`            | Number of reads per FastQ split (0 to disable splitting)                | `integer` | 100000000   |          |        |
-| `trim_front`             | Number of bases to trim from the front of the read                      | `integer` | 0           |          |        |
-| `trim_tail`              | Number of bases to trim from the tail of the read                       | `integer` | 0           |          |        |
-| `adapter_R1`             | Adapter sequence to be trimmed                                          | `string`  | None        |          |        |
-| `adapter_R2`             | Adapter sequence to be trimmed                                          | `string`  | None        |          |        |
-| `disable_picard_metrics` | Disable the calculation of (slow) Picard metrics                        | `boolean` | False       |          |        |
-| `roi`                    | Region of interest for coverage analysis to be applied to all samples   | `string`  | None        |          |        |
-| `genelists`              | Directory containing gene list bed files for granular coverage analysis | `string`  | None        |          |        |
+| Parameter     | Description                                                                                                                                                                                                                                                                                                                                                                                                        | Type      | Default   | Required | Hidden |
+| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------- | --------- | -------- | ------ |
+| `split_fastq` | Specify how many reads each split of a FastQ file contains. Set 0 to turn off splitting at all. <details><summary>Help</summary><small>Use the the tool FastP to split FASTQ file by number of reads. This parallelizes across fastq file shards speeding up mapping. Note although the minimum value is 250 reads, if you have fewer than 250 reads a single FASTQ shard will still be created.</small></details> | `integer` | 100000000 |          |        |
+| `genelists`   | Directory containing gene list bed files for granular coverage analysis                                                                                                                                                                                                                                                                                                                                            | `string`  | None      |          |        |
 
 ## Institutional config options
 
 Parameters used to describe centralised config profiles. These should not be edited.
 
-| Parameter                    | Description                                                                                                                                                                                                                                                                                                                                                                                       | Type     | Default                                                  | Required | Hidden |
-| ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------------------------------------------------------- | -------- | ------ |
-| `custom_config_version`      | Git commit id for Institutional configs.                                                                                                                                                                                                                                                                                                                                                          | `string` | master                                                   |          | True   |
-| `custom_config_base`         | Base directory for Institutional configs. <details><summary>Help</summary><small>If you're running offline, Nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell Nextflow where to find them with this parameter.</small></details> | `string` | https://raw.githubusercontent.com/nf-core/configs/master |          | True   |
-| `config_profile_name`        | Institutional config name.                                                                                                                                                                                                                                                                                                                                                                        | `string` |                                                          |          | True   |
-| `config_profile_description` | Institutional config description.                                                                                                                                                                                                                                                                                                                                                                 | `string` |                                                          |          | True   |
-| `config_profile_contact`     | Institutional config contact information.                                                                                                                                                                                                                                                                                                                                                         | `string` |                                                          |          | True   |
-| `config_profile_url`         | Institutional config URL link.                                                                                                                                                                                                                                                                                                                                                                    | `string` |                                                          |          | True   |
+| Parameter                    | Description                                                                                                                                                                                                                                                                                                                                                                         | Type     | Default                                                | Required | Hidden |
+| ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ------------------------------------------------------ | -------- | ------ |
+| `custom_config_version`      | Git commit id for Institutional configs.                                                                                                                                                                                                                                                                                                                                            | `string` | main                                                   |          | True   |
+| `custom_config_base`         | Base directory for custom configs. <details><summary>Help</summary><small>If you're running offline, Nextflow will not be able to fetch the custom config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell Nextflow where to find them with this parameter.</small></details> | `string` | https://raw.githubusercontent.com/nf-cmgg/configs/main |          | True   |
+| `config_profile_name`        | Institutional config name.                                                                                                                                                                                                                                                                                                                                                          | `string` |                                                        |          | True   |
+| `config_profile_description` | Institutional config description.                                                                                                                                                                                                                                                                                                                                                   | `string` |                                                        |          | True   |
+| `config_profile_contact`     | Institutional config contact information.                                                                                                                                                                                                                                                                                                                                           | `string` |                                                        |          | True   |
+| `config_profile_url`         | Institutional config URL link.                                                                                                                                                                                                                                                                                                                                                      | `string` |                                                        |          | True   |
 
 ## Generic options
 
 Less common options for the pipeline, typically set in a config file.
 
-| Parameter                     | Description                                                                                                                                                                                                                                                                                                                                                                                                  | Type      | Default | Required | Hidden |
-| ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------- | ------- | -------- | ------ |
-| `help`                        | Display help text.                                                                                                                                                                                                                                                                                                                                                                                           | `boolean` |         |          | True   |
-| `version`                     | Display version and exit.                                                                                                                                                                                                                                                                                                                                                                                    | `boolean` |         |          | True   |
-| `publish_dir_mode`            | Method used to save pipeline results to output directory. <details><summary>Help</summary><small>The Nextflow `publishDir` option specifies which intermediate files should be saved to the output directory. This option tells the pipeline what method should be used to move these files. See [Nextflow docs](https://www.nextflow.io/docs/latest/process.html#publishdir) for details.</small></details> | `string`  | copy    |          | True   |
-| `email_on_fail`               | Email address for completion summary, only when pipeline fails. <details><summary>Help</summary><small>An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.</small></details>                                                                                                                                                  | `string`  |         |          | True   |
-| `plaintext_email`             | Send plain-text email instead of HTML.                                                                                                                                                                                                                                                                                                                                                                       | `boolean` |         |          | True   |
-| `max_multiqc_email_size`      | File size limit when attaching MultiQC reports to summary emails.                                                                                                                                                                                                                                                                                                                                            | `string`  | 25.MB   |          | True   |
-| `monochrome_logs`             | Do not use coloured log outputs.                                                                                                                                                                                                                                                                                                                                                                             | `boolean` |         |          | True   |
-| `hook_url`                    | Incoming hook URL for messaging service <details><summary>Help</summary><small>Incoming hook URL for messaging service. Currently, MS Teams and Slack are supported.</small></details>                                                                                                                                                                                                                       | `string`  |         |          | True   |
-| `multiqc_config`              | Custom config file to supply to MultiQC.                                                                                                                                                                                                                                                                                                                                                                     | `string`  |         |          | True   |
-| `multiqc_logo`                | Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file                                                                                                                                                                                                                                                                                                                 | `string`  |         |          | True   |
-| `multiqc_methods_description` | Custom MultiQC yaml file containing HTML including a methods description.                                                                                                                                                                                                                                                                                                                                    | `string`  |         |          |        |
-| `validate_params`             | Boolean whether to validate parameters against the schema at runtime                                                                                                                                                                                                                                                                                                                                         | `boolean` | True    |          | True   |
+| Parameter                      | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | Type                    | Default                                                  | Required | Hidden |
+| ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------- | -------------------------------------------------------- | -------- | ------ |
+| `version`                      | Display version and exit.                                                                                                                                                                                                                                                                                                                                                                                                                                                             | `boolean`               |                                                          |          | True   |
+| `publish_dir_mode`             | Method used to save pipeline results to output directory. (accepted: `symlink`\|`rellink`\|`link`\|`copy`\|`copyNoFollow`\|`move`) <details><summary>Help</summary><small>The Nextflow `publishDir` option specifies which intermediate files should be saved to the output directory. This option tells the pipeline what method should be used to move these files. See [Nextflow docs](https://www.nextflow.io/docs/latest/process.html#publishdir) for details.</small></details> | `string`                | copy                                                     |          | True   |
+| `email_on_fail`                | Email address for completion summary, only when pipeline fails. <details><summary>Help</summary><small>An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.</small></details>                                                                                                                                                                                                                           | `string`                |                                                          |          | True   |
+| `plaintext_email`              | Send plain-text email instead of HTML.                                                                                                                                                                                                                                                                                                                                                                                                                                                | `boolean`               |                                                          |          | True   |
+| `max_multiqc_email_size`       | File size limit when attaching MultiQC reports to summary emails.                                                                                                                                                                                                                                                                                                                                                                                                                     | `string`                | 25.MB                                                    |          | True   |
+| `monochrome_logs`              | Do not use coloured log outputs.                                                                                                                                                                                                                                                                                                                                                                                                                                                      | `boolean`               |                                                          |          | True   |
+| `hook_url`                     | Incoming hook URL for messaging service <details><summary>Help</summary><small>Incoming hook URL for messaging service. Currently, MS Teams and Slack are supported.</small></details>                                                                                                                                                                                                                                                                                                | `string`                |                                                          |          | True   |
+| `multiqc_config`               | Custom config file to supply to MultiQC.                                                                                                                                                                                                                                                                                                                                                                                                                                              | `string`                |                                                          |          | True   |
+| `multiqc_logo`                 | Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file                                                                                                                                                                                                                                                                                                                                                                                          | `string`                |                                                          |          | True   |
+| `multiqc_methods_description`  | Custom MultiQC yaml file containing HTML including a methods description.                                                                                                                                                                                                                                                                                                                                                                                                             | `string`                |                                                          |          |        |
+| `validate_params`              | Boolean whether to validate parameters against the schema at runtime                                                                                                                                                                                                                                                                                                                                                                                                                  | `boolean`               | True                                                     |          | True   |
+| `pipelines_testdata_base_path` | Base URL or local path to location of pipeline test dataset files                                                                                                                                                                                                                                                                                                                                                                                                                     | `string`                | https://raw.githubusercontent.com/nf-core/test-datasets/ |          | True   |
+| `trace_report_suffix`          | Suffix to add to the trace report filename. Default is the date and time in the format yyyy-MM-dd_HH-mm-ss.                                                                                                                                                                                                                                                                                                                                                                           | `string`                |                                                          |          | True   |
+| `help`                         | Display the help message.                                                                                                                                                                                                                                                                                                                                                                                                                                                             | `['boolean', 'string']` |                                                          |          |        |
+| `help_full`                    | Display the full detailed help message.                                                                                                                                                                                                                                                                                                                                                                                                                                               | `boolean`               |                                                          |          |        |
+| `show_hidden`                  | Display hidden parameters in the help message (only works when --help or --help_full are provided).                                                                                                                                                                                                                                                                                                                                                                                   | `boolean`               |                                                          |          |        |
+| `igenomes_base`                | Directory / URL base for iGenomes references.                                                                                                                                                                                                                                                                                                                                                                                                                                         | `string`                | /references/                                             |          | True   |
+| `igenomes_ignore`              | Do not load the iGenomes reference config. <details><summary>Help</summary><small>Do not load `igenomes.config` when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in `igenomes.config`.</small></details>                                                                                                                                                                                                     | `boolean`               |                                                          |          | True   |
+| `genome`                       | Name of iGenomes reference. <details><summary>Help</summary><small>If using a reference genome configured in the pipeline using iGenomes, use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. `--genome GRCh38`. <br><br>See the [nf-core website docs](https://nf-co.re/usage/reference_genomes) for more details.</small></details>                                                         | `string`                |                                                          |          |        |
diff --git a/docs/usage.md b/docs/usage.md
index 32028ecd..c44bccca 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -116,7 +116,7 @@ CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
 The typical command for running the pipeline is as follows:
 
 ```bash
-nextflow run nf-cmgg/preprocessing --input ./samplesheet.csv --outdir ./results -profile docker
+nextflow run nf-cmgg/preprocessing --input ./samplesheet.<csv|json|yaml> --outdir ./results -profile docker
 ```
 
 This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.
@@ -195,6 +195,12 @@ They are loaded in sequence, so later profiles can overwrite earlier profiles.
 
 If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer enviroment.
 
+- `debug`
+  - A generic profile with settings to help with debugging the pipeline. It will use more verbose logging.
+- `arm64`
+  - A generic profile with settings to run the pipeline on ARM64 architecture machines (eg. Apple Silicon). It will use software containers built for ARM64 where available.
+- `emulate_amd64`
+  - A generic profile with settings to run the pipeline on ARM64 architecture machines (eg. Apple Silicon) using AMD64 software containers. This is for when ARM64 containers are not available but you still want to run the pipeline on an ARM64 machine. Note that this will be slower than using ARM64 containers.
 - `test`
   - A profile with a complete configuration for automated testing
   - Includes links to test data so needs no other parameters
@@ -241,22 +247,14 @@ A pipeline might not always support every possible argument or option of a parti
 
 To learn how to provide additional arguments to a particular tool of the pipeline, please see the [customising tool arguments](https://nf-co.re/docs/usage/configuration#customising-tool-arguments) section of the nf-core website.
 
-### nf-core/configs
+### nf-core/configs and nf-cmgg/configs
 
-In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter. You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile.
+In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-cmgg pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter. You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile.
 
 See the main [Nextflow documentation](https://www.nextflow.io/docs/latest/config.html) for more information about creating your own configuration files.
 
 If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack) on the [`#configs` channel](https://nfcore.slack.com/channels/configs).
 
-## Azure Resource Requests
-
-To be used with the `azurebatch` profile by specifying the `-profile azurebatch`.
-We recommend providing a compute `params.vm_type` of `Standard_D16_v3` VMs by default but these options can be changed if required.
-
-Note that the choice of VM size depends on your quota and the overall workload during the analysis.
-For a thorough list, please refer the [Azure Sizes for virtual machines in Azure](https://docs.microsoft.com/en-us/azure/virtual-machines/sizes).
-
 ## Running in the background
 
 Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.
diff --git a/nextflow.config b/nextflow.config
index 44d9698c..990ff21a 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -69,26 +69,6 @@ profiles {
         cleanup                                      = false
         nextflow.enable.configProcessNamesValidation = true
     }
-    conda {
-        conda.enabled        = true
-        docker.enabled       = false
-        singularity.enabled  = false
-        podman.enabled       = false
-        shifter.enabled      = false
-        charliecloud.enabled = false
-        conda.channels       = ['conda-forge', 'bioconda']
-        apptainer.enabled    = false
-    }
-    mamba {
-        conda.enabled        = true
-        conda.useMamba       = true
-        docker.enabled       = false
-        singularity.enabled  = false
-        podman.enabled       = false
-        shifter.enabled      = false
-        charliecloud.enabled = false
-        apptainer.enabled    = false
-    }
     docker {
         docker.enabled       = true
         conda.enabled        = false

From 085ae8eaae20bacb33693107a13ef2da2c77fcc0 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 09:52:37 +0100
Subject: [PATCH 17/24] rework metro map

---
 docs/workflow-dark.mmd | 175 +++++++++++------------------------------
 1 file changed, 45 insertions(+), 130 deletions(-)

diff --git a/docs/workflow-dark.mmd b/docs/workflow-dark.mmd
index 5005df0b..4c4458e3 100644
--- a/docs/workflow-dark.mmd
+++ b/docs/workflow-dark.mmd
@@ -1,143 +1,58 @@
 %%metro title: nf-cmgg/preprocessing
-%%metro logo: images/nf-cmgg-preprocessing_logo_dark.png
-%%metro style: dark
-
-%%metro line: main | Main | #4CAF50
-%%metro line: align_rna | RNA Alignment | #FF9800
-%%metro line: align_dna | DNA Alignment | #ff1e00ff
-%%metro line: align_postprocessing | Alignment Postprocessing | #9C27B0
-%%metro line: qc | Quality Control | #2196F3
-
-%%metro file: fastq_in | FASTQ
-%%metro file: bcl_in | BCL
-%%metro file: cram_out | CRAM
-%%metro file: fastq_out | FASTQ
-%%metro file: reports_out | HTML
-
-graph LR
-    %% define nodes
-    bcl_in[]
-    fastq_in[]
-    fastq_out[]
-    cram_out[]
-    reports_out[]
-
-    subgraph demultiplexing
-        %%metro exit: right | main
-        %%metro exit: bottom | qc
-        bcl_in[BCL]
-        bclconvert[bcl-convert]
-        bcl_in -->|main| bclconvert
+%%metro style: nf-core
+%%metro line: demux | Demultiplexing | #0000ff
+%%metro line: alignment | Alignment and Postprocessing | #00ff00
+%%metro line: qc | Quality control | #ff0000
+%%metro file: BCL_IN | BCL
+%%metro file: FASTQ_IN | FASTQ
+%%metro line_order: span
+%%metro compact_offsets: true
+
+graph TD
+    BCL_IN[]
+    FASTQ_IN[]
+    MULTIQC_LIBRARY
+    MULTIQC_SAV
+
+    subgraph Demultiplexing
+        BCL_IN -->|demux| BCLCONVERT
+        BCL_IN -->|qc| MULTIQC_SAV
+        BCLCONVERT -->|qc| MULTIQC_SAV
     end
 
-    subgraph fastq_qc
-        fastp[fastp]
-        falco[falco]
-        bclconvert -->|main| fastp
-        bclconvert -->|main| fastq_out
-        bclconvert -->|qc| falco
-        fastq_in -->|main| fastp
+    subgraph FASTQ_QC
+        FASTQ_IN -->|qc| FALCO
+        FASTQ_IN -->|qc| FASTP
+        BCLCONVERT -->|qc| FALCO
+        BCLCONVERT -->|qc| FASTP
+        FALCO -->|qc| MULTIQC_LIBRARY
+        FASTP -->|qc| MULTIQC_LIBRARY
     end
 
-    subgraph alignment_dna
-        bwamem[BWA-MEM]
-        bwamem2[BWA-MEM2]
-        dragmap[Dragmap]
-        snapaligner[SNAP]
-        strobealign[StrobeAlign]
-        _dna_bam_out[]
+    _CRAM
 
-        fastp -->|align_dna| bwamem
-        fastp -->|align_dna| bwamem2
-        fastp -->|align_dna| dragmap
-        fastp -->|align_dna| snapaligner
-        fastp -->|align_dna| strobealign
-    end
+    subgraph Alignment_and_Postprocessing
+        FASTQ_IN -->|alignment| ALIGN
+        BCLCONVERT -->|alignment| ALIGN
 
-    subgraph alignment_rna
-        star[STAR]
-        _rna_bam_out[]
-
-        fastp -->|align_rna| star
+        ALIGN -->|alignment| MARKDUP
+        MARKDUP -->|alignment| _CRAM
     end
 
-    subgraph alignment_postprocessing
-        _postprocess_bam_in[]
-        bamsormadup[bamsormadup]
-        samtools_sort[samtools sort]
-        samtools_sormadup[samtools sormadup]
-        samtools_convert[samtools convert]
-
-        bwamem -->|align_postprocessing| _postprocess_bam_in
-        bwamem2 -->|align_postprocessing| _postprocess_bam_in
-        dragmap -->|align_postprocessing| _postprocess_bam_in
-        snapaligner -->|align_postprocessing| _postprocess_bam_in
-        strobealign -->|align_postprocessing| _postprocess_bam_in
-        star -->|align_postprocessing| _postprocess_bam_in
-
-        _postprocess_bam_in -->|align_postprocessing| bamsormadup
-        _postprocess_bam_in -->|align_postprocessing| samtools_sort
-        _postprocess_bam_in -->|align_postprocessing| samtools_sormadup
-        _postprocess_bam_in -->|align_postprocessing| samtools_convert
-
-        bamsormadup -->|align_postprocessing| samtools_convert
+    _QC_COLLECT
 
-        samtools_convert -->|align_postprocessing| cram_out
-        samtools_sort -->|align_postprocessing| cram_out
-        samtools_sormadup -->|align_postprocessing| cram_out
+    subgraph COVERAGE_QC
+        _CRAM -->|qc| MOSDEPTH
+        _CRAM -->|qc| SAMTOOLS_COV
+        MOSDEPTH -->|qc| _QC_COLLECT
+        SAMTOOLS_COV -->|qc| _QC_COLLECT
     end
 
-   subgraph alignment_qc
-        %%metro direction: TB
-        samtools_flagstat[samtools flagstat]
-        samtools_stats[samtools stats]
-        samtools_idxstats[samtools idxstats]
-        picard_collectmultiplemetrics[picard collectmultiplemetrics]
-
-        cram_out -->|qc| samtools_flagstat
-        cram_out -->|qc| samtools_stats
-        cram_out -->|qc| samtools_idxstats
-        cram_out -->|qc| picard_collectmultiplemetrics
-   end
-
-   subgraph alignment_qc_wgs
-        picard_collectwgsmetrics[picard collectwgsmetrics]
-        cram_out -->|qc| picard_collectwgsmetrics
-   end
-
-   subgraph alignment_qc_targetted
-        picard_collecthsmetrics[picard collecthsmetrics]
-        cram_out -->|qc| picard_collecthsmetrics
-   end
-
-   subgraph coverage_qc
-        mosdepth[mosdepth]
-        samtools_coverage[samtools coverage]
-        panelcoverage[panelcoverage]
-
-        cram_out -->|qc| mosdepth
-        cram_out -->|qc| samtools_coverage
-        cram_out -->|qc| panelcoverage
-   end
-
-    subgraph reporting
-    mqc_sav[MultiQC-SAV]
-    mqc_library[Library MultiQC]
-    reports_out[QC Reports]
-
-    bclconvert -->|qc| mqc_sav
-    fastp -->|qc| mqc_library
-    falco -->|qc| mqc_library
-    samtools_flagstat -->|qc| mqc_library
-    samtools_stats -->|qc| mqc_library
-    samtools_idxstats -->|qc| mqc_library
-    picard_collectmultiplemetrics -->|qc| mqc_library
-    picard_collectwgsmetrics -->|qc| mqc_library
-    picard_collecthsmetrics -->|qc| mqc_library
-    mosdepth -->|qc| mqc_library
-    samtools_coverage -->|qc| mqc_library
-    panelcoverage -->|qc| mqc_library
-
-    mqc_sav -->|qc| reports_out
-    mqc_library -->|qc| reports_out
+    subgraph BAM_QC
+        _CRAM -->|qc| SAMTOOLS_QC
+        _CRAM -->|qc| PICARD
+        SAMTOOLS_QC -->|qc| _QC_COLLECT
+        PICARD -->|qc| _QC_COLLECT
     end
+
+    _QC_COLLECT -->|qc| MULTIQC_LIBRARY

From bf9a2ebc388dbb17e84c8c7a43980d947f25233b Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 09:54:51 +0100
Subject: [PATCH 18/24] add plugins untill NF 26.XX is in prod

---
 nextflow.config | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/nextflow.config b/nextflow.config
index 990ff21a..61685c2d 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -248,6 +248,8 @@ manifest {
 
 // Nextflow plugins
 plugins {
+    id 'nf-cgroup-metrics@1.0.1'
+    id 'nf-cmgg@0.1.0'
     id 'nf-schema@2.6.1'
     id 'nf-teams@0.1.0'
 }
@@ -258,6 +260,10 @@ validation {
 }
 
 // TODO remove this once minimal nf-version is 26.0X with strict syntax.
+cmgg {
+    samplesheets.enabled = true
+    done.enabled         = true
+}
 teams {
     enabled            = true
     webHook {

From c942c635cee396b1cd57a6b5fdeac375765e8662 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 10:07:34 +0100
Subject: [PATCH 19/24] Remove download_pipeline workflow file

---
 .github/workflows/download_pipeline.yml | 134 ------------------------
 1 file changed, 134 deletions(-)
 delete mode 100644 .github/workflows/download_pipeline.yml

diff --git a/.github/workflows/download_pipeline.yml b/.github/workflows/download_pipeline.yml
deleted file mode 100644
index d9970752..00000000
--- a/.github/workflows/download_pipeline.yml
+++ /dev/null
@@ -1,134 +0,0 @@
-name: Test successful pipeline download with 'nf-core pipelines download'
-
-# Run the workflow when:
-#  - dispatched manually
-#  - when a PR is opened or reopened to main/master branch
-#  - the head branch of the pull request is updated, i.e. if fixes for a release are pushed last minute to dev.
-on:
-  workflow_dispatch:
-    inputs:
-      testbranch:
-        description: "The specific branch you wish to utilize for the test execution of nf-core pipelines download."
-        required: true
-        default: "dev"
-  pull_request:
-    branches:
-      - main
-      - master
-
-env:
-  NXF_ANSI_LOG: false
-
-jobs:
-  configure:
-    runs-on: ubuntu-latest
-    outputs:
-      REPO_LOWERCASE: ${{ steps.get_repo_properties.outputs.REPO_LOWERCASE }}
-      REPOTITLE_LOWERCASE: ${{ steps.get_repo_properties.outputs.REPOTITLE_LOWERCASE }}
-      REPO_BRANCH: ${{ steps.get_repo_properties.outputs.REPO_BRANCH }}
-    steps:
-      - name: Get the repository name and current branch
-        id: get_repo_properties
-        run: |
-          echo "REPO_LOWERCASE=${GITHUB_REPOSITORY,,}" >> "$GITHUB_OUTPUT"
-          echo "REPOTITLE_LOWERCASE=$(basename ${GITHUB_REPOSITORY,,})" >> "$GITHUB_OUTPUT"
-          echo "REPO_BRANCH=${{ github.event.inputs.testbranch || 'dev' }}" >> "$GITHUB_OUTPUT"
-
-  download:
-    runs-on: ubuntu-latest
-    needs: configure
-    steps:
-      - name: Install Nextflow
-        uses: nf-core/setup-nextflow@v2
-
-      - name: Disk space cleanup
-        uses: jlumbroso/free-disk-space@54081f138730dfa15788a46383842cd2f914a1be # v1.3.1
-
-      - uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6
-        with:
-          python-version: "3.14"
-          architecture: "x64"
-
-      - name: Setup Apptainer
-        uses: eWaterCycle/setup-apptainer@4bb22c52d4f63406c49e94c804632975787312b3 # v2.0.0
-        with:
-          apptainer-version: 1.3.4
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install git+https://github.com/nf-core/tools.git
-
-      - name: Make a cache directory for the container images
-        run: |
-          mkdir -p ./singularity_container_images
-
-      - name: Download the pipeline
-        env:
-          NXF_SINGULARITY_CACHEDIR: ./singularity_container_images
-        run: |
-          nf-core pipelines download ${{ needs.configure.outputs.REPO_LOWERCASE }} \
-          --revision ${{ needs.configure.outputs.REPO_BRANCH }} \
-          --outdir ./${{ needs.configure.outputs.REPOTITLE_LOWERCASE }} \
-          --compress "none" \
-          --container-system 'singularity' \
-          --container-library "quay.io" -l "docker.io" -l "community.wave.seqera.io/library/" \
-          --container-cache-utilisation 'amend' \
-          --download-configuration 'yes'
-
-      - name: Inspect download
-        run: tree ./${{ needs.configure.outputs.REPOTITLE_LOWERCASE }}
-
-      - name: Inspect container images
-        run: tree ./singularity_container_images | tee ./container_initial
-
-      - name: Count the downloaded number of container images
-        id: count_initial
-        run: |
-          image_count=$(ls -1 ./singularity_container_images | wc -l | xargs)
-          echo "Initial container image count: $image_count"
-          echo "IMAGE_COUNT_INITIAL=$image_count" >> "$GITHUB_OUTPUT"
-
-      - name: Run the downloaded pipeline (stub)
-        id: stub_run_pipeline
-        continue-on-error: true
-        env:
-          NXF_SINGULARITY_CACHEDIR: ./singularity_container_images
-          NXF_SINGULARITY_HOME_MOUNT: true
-        run: nextflow run ./${{needs.configure.outputs.REPOTITLE_LOWERCASE }}/$( sed 's/\W/_/g' <<< ${{ needs.configure.outputs.REPO_BRANCH }}) -stub -profile test,singularity,s3_ugent --outdir ./results
-      - name: Run the downloaded pipeline (stub run not supported)
-        id: run_pipeline
-        if: ${{ steps.stub_run_pipeline.outcome == 'failure' }}
-        env:
-          NXF_SINGULARITY_CACHEDIR: ./singularity_container_images
-          NXF_SINGULARITY_HOME_MOUNT: true
-        run: nextflow run ./${{ needs.configure.outputs.REPOTITLE_LOWERCASE }}/$( sed 's/\W/_/g' <<< ${{ needs.configure.outputs.REPO_BRANCH }}) -profile test,singularity,s3_ugent --outdir ./results
-
-      - name: Count the downloaded number of container images
-        id: count_afterwards
-        run: |
-          image_count=$(ls -1 ./singularity_container_images | wc -l | xargs)
-          echo "Post-pipeline run container image count: $image_count"
-          echo "IMAGE_COUNT_AFTER=$image_count" >> "$GITHUB_OUTPUT"
-
-      - name: Compare container image counts
-        id: count_comparison
-        run: |
-          if [ "${{ steps.count_initial.outputs.IMAGE_COUNT_INITIAL }}" -ne "${{ steps.count_afterwards.outputs.IMAGE_COUNT_AFTER }}" ]; then
-            initial_count=${{ steps.count_initial.outputs.IMAGE_COUNT_INITIAL }}
-            final_count=${{ steps.count_afterwards.outputs.IMAGE_COUNT_AFTER }}
-            difference=$((final_count - initial_count))
-            echo "$difference additional container images were \n downloaded at runtime . The pipeline has no support for offline runs!"
-            tree ./singularity_container_images > ./container_afterwards
-            diff ./container_initial ./container_afterwards
-            exit 1
-          else
-            echo "The pipeline can be downloaded successfully!"
-          fi
-
-      - name: Upload Nextflow logfile for debugging purposes
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
-        with:
-          name: nextflow_logfile.txt
-          path: .nextflow.log*
-          include-hidden-files: true

From c2c97712a33f83089d3958959ea9ab3cd80bbf9a Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 10:38:52 +0100
Subject: [PATCH 20/24] Update usage

---
 assets/schema_input.json |   4 +-
 docs/usage.md            | 133 ++++++++++++++++++++-------------------
 2 files changed, 70 insertions(+), 67 deletions(-)

diff --git a/assets/schema_input.json b/assets/schema_input.json
index a6cd4941..4a7bd527 100644
--- a/assets/schema_input.json
+++ b/assets/schema_input.json
@@ -160,10 +160,10 @@
         },
         "anyOf": [
             {
-                "required": ["id", "samplename", "organism", "aligner", "tag", "fastq_1", "fastq_2"]
+                "required": ["id", "samplename", "organism", "aligner", "fastq_1", "fastq_2"]
             },
             {
-                "required": ["id", "samplename", "genome", "aligner", "tag", "fastq_1", "fastq_2"]
+                "required": ["id", "samplename", "genome", "aligner", "fastq_1", "fastq_2"]
             },
             {
                 "required": ["id", "samplesheet", "sample_info", "flowcell"]
diff --git a/docs/usage.md b/docs/usage.md
index c44bccca..3e64d578 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -14,42 +14,56 @@ You will need to create a samplesheet with information about the samples you wou
 
 The pipeline supports two types of samplesheets to be used as input: [`fastq`](#fastq-samplesheet) and [`flowcell`](#flowcell-samplesheet) samplesheets. The type will be automatically detected and applied by the pipeline. The pipeline will also auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire.
 
-### Common samplesheet fields
-
-This table shows all samplesheet fields that can be used by both the [`fastq`](#fastq-samplesheet) and the [`flowcell`](#flowcell-samplesheet) samplesheet types.
-
-| Column | Description                                                                        | Required for Fastq | Required for Flowcell |
-| ------ | ---------------------------------------------------------------------------------- | ------------------ | --------------------- |
-| `id`   | Unique samplesheet/flowcell ID. Can only contain letters, numbers and underscores. | :heavy_check_mark: | :heavy_check_mark:    |
-
 ### Fastq samplesheet
 
-A `fastq` samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where `TREATMENT_REP3` has been sequenced twice.
-
-```csv title="samplesheet.csv"
-id,samplename,fastq_1,fastq_2,genome,tag
-CONTROL_REP1,CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,GRCh38,WES
-CONTROL_REP2,CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz,GRCh38,WES
-CONTROL_REP3,CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz,GRCh38,WES
-TREATMENT_REP1,TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,,GRCh38,WES
-TREATMENT_REP2,TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,,GRCh38,WES
-TREATMENT_REP3,TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,,GRCh38,WES
-TREATMENT_REP3,TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,,GRCh38,WES
+A `fastq` samplesheet file consisting of paired-end data may look something like the one below.
+
+```yml
+- id: DNA1_L001
+  samplename: DNA_paired1
+  library: test_library
+  genome: GRCh38
+  aligner: bwamem
+  markdup: bamsormadup
+  umi_aware: false
+  skip_trimming: false
+  trim_front: 0
+  trim_tail: 0
+  adapter_R1: AGATCGGAAGAGCACACGTCTGAACTCCTTA
+  adapter_R2: AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+  run_coverage: true
+  disable_picard_metrics: false
+  roi: null
+  tag: WES
+  sample_type: DNA
+  fastq_1: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R1.fastq.gz
+  fastq_2: https://github.com/nf-cmgg/test-datasets/raw/preprocessing/data/genomics/homo_sapiens/illumina/fastq/sample1_R2.fastq.gz
 ```
 
 Following table shows the fields that are used by the `fastq` samplesheet:
 
-| Column       | Description                                                                                                                                  | Required                                        |
-| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- |
-| `fastq_1`    | FastQ file for reads 1 must be provided, cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                               | :heavy_check_mark:                              |
-| `fastq_2`    | FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                                                 | :x:                                             |
-| `samplename` | The sample name corresponding to the sample in the Fastq file(s)                                                                             | :heavy_check_mark:                              |
-| `genome`     | The genome build to use for the analysis. Currently supports GRCh38, GRCm39 and GRCz11                                                       | :heavy_check_mark: (unless `organism` is given) |
-| `organism`   | Full name of the organism. Currently supports "Homo sapiens", "Mus musculus" and "Danio rerio"                                               | :heavy_check_mark: (unless `genome` is given)   |
-| `library`    | Sample library name                                                                                                                          | :x:                                             |
-| `tag`        | The tag used by the sample. Can be one of WES, WGS or coPGT-M                                                                                | :heavy_check_mark:                              |
-| `roi`        | The path to a BED file containing <b>R</b>egions <b>O</b>f <b>I</b>nterest for coverage analysis                                             | :x:                                             |
-| `aligner`    | The aligner to use for this sample. Can be one of these: bowtie2, bwamem, bwamem2, dragmap, strobe and snap. set to `false` to output fastq. | :x:                                             |
+| Column                   | Description                                                                                                                                              | Required                                        |
+| ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- |
+| `id`                     | Unique sample identifier                                                                                                                                 | :heavy_check_mark:                              |
+| `samplename`             | The sample name corresponding to the sample in the Fastq file(s)                                                                                         | :heavy_check_mark:                              |
+| `genome`                 | The genome build to use for the analysis. Currently supports `GRCh38`, `GRCm39` and `GRCz11`                                                             | :heavy_check_mark: (unless `organism` is given) |
+| `organism`               | Full name of the organism. Currently supports `Homo sapiens`, `Mus musculus` and `Danio rerio`                                                           | :heavy_check_mark: (unless `genome` is given)   |
+| `library`                | Sample library name                                                                                                                                      | :x:                                             |
+| `tag`                    | The tag used by the sample. Can be one of `WES`, `WGS`, `SeqCap` and `coPGT-M`                                                                           | :x:                                             |
+| `aligner`                | The aligner to use for this sample. Can be one of these: `bowtie2`, `bwamem`, `bwamem2`, `dragmap`, `strobe` and `snap`. Set to `false` to output fastq. | :heavy_check_mark:                              |
+| `markdup`                | Markdup algorithm to use for duplicate marking. Can be set to `bamsormadup`, `samtools` or `false`                                                       | :x:                                             |
+| `umi_aware`              | Whether UMI-aware processing should be used. Only applies when `markdup` is set to `samtools`                                                            | :x:                                             |
+| `skip_trimming`          | Skip adapter trimming step                                                                                                                               | :x:                                             |
+| `trim_front`             | Number of bases to trim from the front of reads                                                                                                          | :x:                                             |
+| `trim_tail`              | Number of bases to trim from the tail of reads                                                                                                           | :x:                                             |
+| `adapter_R1`             | Adapter sequence for read 1                                                                                                                              | :x:                                             |
+| `adapter_R2`             | Adapter sequence for read 2                                                                                                                              | :x:                                             |
+| `run_coverage`           | Run coverage analysis                                                                                                                                    | :x:                                             |
+| `disable_picard_metrics` | Disable Picard metrics collection                                                                                                                        | :x:                                             |
+| `roi`                    | The path to a BED file containing Regions Of Interest for coverage analysis                                                                              | :x:                                             |
+| `sample_type`            | Sample type (e.g., `DNA`, `RNA`)                                                                                                                         | :x:                                             |
+| `fastq_1`                | FastQ file for reads 1 must be provided, cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                                           | :heavy_check_mark:                              |
+| `fastq_2`                | FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                                                             | :x:                                             |
 
 An [example samplesheet](../tests/inputs/test.yml) has been provided with the pipeline.
 
@@ -57,9 +71,12 @@ An [example samplesheet](../tests/inputs/test.yml) has been provided with the pi
 
 A `flowcell` samplesheet file consisting of one sequencing run may look something like the one below.
 
-```csv title="samplesheet.csv"
-id,samplesheet,sample_info,flowcell
-RUN_NAME,RUN_NAME_samplesheet.csv,RUN_NAME_sampleinfo.csv,RUN_NAME_flowcell/
+```yml
+- id: 200624_A00834_0183_BHMTFYDRXX
+  samplesheet: https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleSheet_2.csv
+  lane: 1
+  flowcell: s3://test-data/genomics/homo_sapiens/illumina/bcl/
+  sample_info: https://github.com/nf-cmgg/test-datasets/raw/refs/heads/preprocessing/data/genomics/homo_sapiens/illumina/flowcell/SampleInfo_2.json
 ```
 
 Following table shows the fields that are used by the `flowcell` samplesheet:
@@ -77,38 +94,24 @@ An [example samplesheet](../tests/inputs/test.yml) has been provided with the pi
 
 A `flowcell` sample info JSON/YML file consisting for one sequencing run may look something like the one below.
 
-```json title="sample_info.json"
-{
-  "samplename": "Sample1",
-  "library": "test",
-  "organism": "Homo sapiens",
-  "tag": "WES"
-}
-```
-
-Following table shows the fields that are used by the `flowcell` samplesheet:
-
-| Column          | Description                                                                                                                                  | Required           |
-| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ |
-| `samplename`    | The sample name                                                                                                                              | :heavy_check_mark: |
-| `library`       | The library name                                                                                                                             | :x:                |
-| `tag`           | Sample tag. Has to be one of these: WES, WGS, coPGT-M                                                                                        | :heavy_check_mark: |
-| `organism`      | The organism of the sample. Has to be one of these: "Homo sapiens", "Mus musculus" or "Danio rerio"                                          | :heavy_check_mark: |
-| `vivar_project` | The vivar project name (currently not used by the pipeline)                                                                                  | :x:                |
-| `binsize`       | The binsize for CNV analysis (currently not used by the pipeline)                                                                            | :x:                |
-| `panels`        | A list of panels for coverage analysis                                                                                                       | :x:                |
-| `roi`           | Region of interest BED file for coverage analysis                                                                                            | :x:                |
-| `aligner`       | The aligner to use for this sample. Can be one of these: bowtie2, bwamem, bwamem2, dragmap, strobe and snap. Set to `false` to output fastq. | :x:                |
-
-### Multiple runs of the same sample
-
-The `sample` identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:
-
-```csv title="samplesheet.csv"
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
+```yml
+- id: DNA1_L001
+  samplename: DNA_paired1
+  library: test_library
+  genome: GRCh38
+  aligner: bwamem
+  markdup: bamsormadup
+  umi_aware: false
+  skip_trimming: false
+  trim_front: 0
+  trim_tail: 0
+  adapter_R1: AGATCGGAAGAGCACACGTCTGAACTCCTTA
+  adapter_R2: AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+  run_coverage: true
+  disable_picard_metrics: false
+  roi: null
+  tag: WES
+  sample_type: DNA
 ```
 
 ## Running the pipeline

From 60d9370a0279396b9d4e19977b2bbb748315e1bf Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 10:53:16 +0100
Subject: [PATCH 21/24] more docs

---
 README.md                                     |   48 +-
 docs/images/metro_map.png                     |  Bin 212847 -> 0 bytes
 docs/images/metro_map.svg                     | 1263 -----------------
 .../metro_map_dark.mmd}                       |    0
 .../metro_map_dark.svg}                       |    0
 docs/images/metro_map_light.mmd               |   58 +
 docs/images/metro_map_light.svg               |  264 ++++
 7 files changed, 339 insertions(+), 1294 deletions(-)
 delete mode 100644 docs/images/metro_map.png
 delete mode 100644 docs/images/metro_map.svg
 rename docs/{workflow-dark.mmd => images/metro_map_dark.mmd} (100%)
 rename docs/{workflow-dark.svg => images/metro_map_dark.svg} (100%)
 create mode 100644 docs/images/metro_map_light.mmd
 create mode 100644 docs/images/metro_map_light.svg

diff --git a/README.md b/README.md
index 119479bf..408d1a51 100644
--- a/README.md
+++ b/README.md
@@ -20,15 +20,22 @@ The pipeline is built using Nextflow, a workflow tool to run tasks across multip
 
 Steps inlcude:
 
-1. Demultiplexing using [`BCLconvert`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html)
-2. Read QC and trimming using [`fastp`](https://github.com/OpenGene/fastp)
-3. Alignment using either [`bwa`](https://github.com/lh3/bwa), [`bwa-mem2`](https://github.com/bwa-mem2/bwa-mem2), [`bowtie2`](https://github.com/BenLangmead/bowtie2), [`dragmap`](https://github.com/Illumina/DRAGMAP), [`snap`](https://github.com/amplab/snap) or [`strobe`](https://github.com/ksahlin/strobealign) for DNA-seq and [`STAR`](https://github.com/alexdobin/STAR) for RNA-seq
-4. Duplicate marking using [`bamsormadup`](https://gitlab.com/german.tischler/biobambam2) or [`samtools markdup`](http://www.htslib.org/doc/samtools-markdup.html)
-5. Coverage analysis using [`mosdepth`](https://github.com/brentp/mosdepth) and [`samtools coverage`](http://www.htslib.org/doc/samtools-coverage.html)
-6. Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollecHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)
-7. QC aggregation using [`multiqc`](https://multiqc.info/)
+- Demultiplexing using [`BCLconvert`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html)
+- Run QC using [`MultiQC SAV`](https://github.com/MultiQC/MultiQC_SAV)
+- Read QC and trimming using [`fastp`](https://github.com/OpenGene/fastp) or [`falco`](https://github.com/smithlabcode/falco)
+- Alignment using either [`bwa`](https://github.com/lh3/bwa), [`bwa-mem2`](https://github.com/bwa-mem2/bwa-mem2), [`bowtie2`](https://github.com/BenLangmead/bowtie2), [`dragmap`](https://github.com/Illumina/DRAGMAP), [`snap`](https://github.com/amplab/snap) or [`strobe`](https://github.com/ksahlin/strobealign) for DNA-seq and [`STAR`](https://github.com/alexdobin/STAR) for RNA-seq
+- Duplicate marking using [`bamsormadup`](https://gitlab.com/german.tischler/biobambam2) or [`samtools markdup`](http://www.htslib.org/doc/samtools-markdup.html)
+- Coverage analysis using [`mosdepth`](https://github.com/brentp/mosdepth) and [`samtools coverage`](http://www.htslib.org/doc/samtools-coverage.html)
+- Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollecHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)
+- QC aggregation using [`multiqc`](https://multiqc.info/)
 
 ![metro map](docs/images/metro_map.png)
+<picture>
+
+  <source media="(prefers-color-scheme: dark)" srcset="docs/images/metro_map_dark.svg">
+  <source media="(prefers-color-scheme: light)" srcset="docs/images/metro_map_light.svg">
+  <img alt="Fallback image description" src="docs/images/metro_map_light.svg">
+</picture>
 
 ## Usage
 
@@ -37,36 +44,15 @@ Steps inlcude:
 
 The full documentation can be found [here](docs/README.md)
 
-First, prepare a samplesheet with your input data that looks as follows:
-
-`samplesheet.csv` for fastq inputs:
-
-```csv
-id,samplename,organism,library,aligner,fastq_1,fastq_2
-sample1,sample1,Homo sapiens,Library_Name,bwamem,reads1.fq.gz,reads2.fq.gz
-```
-
-`samplesheet.csv` for flowcell inputs:
-
-```csv
-id,samplesheet,lane,flowcell,sample_info
-flowcell_id,/path/to/illumina_samplesheet.csv,1,/path/to/sequencer_uploaddir,/path/to/sampleinfo.csv
-```
-
-`sampleinfo.csv` for use with flowcell inputs:
-
-```csv
-samplename,library,organism,tag,aligner
-fc_sample1,test,Homo sapiens,WES,bwamem
-```
+First, prepare a samplesheet with your input data. Check the [usage docs](docs/usage.md) for details on the required format and example files.
 
 Now, you can run the pipeline using:
 
 ```bash
 nextflow run nf-cmgg/preprocessing \
-   -profile <docker/singularity/.../institute> \
+   -profile <docker/singularity/...> \
    --igenomes_base /path/to/genomes \
-   --input samplesheet.csv \
+   --input samplesheet.<csv|yaml|json> \
    --outdir <OUTDIR>
 ```
 
diff --git a/docs/images/metro_map.png b/docs/images/metro_map.png
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diff --git a/docs/workflow-dark.mmd b/docs/images/metro_map_dark.mmd
similarity index 100%
rename from docs/workflow-dark.mmd
rename to docs/images/metro_map_dark.mmd
diff --git a/docs/workflow-dark.svg b/docs/images/metro_map_dark.svg
similarity index 100%
rename from docs/workflow-dark.svg
rename to docs/images/metro_map_dark.svg
diff --git a/docs/images/metro_map_light.mmd b/docs/images/metro_map_light.mmd
new file mode 100644
index 00000000..4c4458e3
--- /dev/null
+++ b/docs/images/metro_map_light.mmd
@@ -0,0 +1,58 @@
+%%metro title: nf-cmgg/preprocessing
+%%metro style: nf-core
+%%metro line: demux | Demultiplexing | #0000ff
+%%metro line: alignment | Alignment and Postprocessing | #00ff00
+%%metro line: qc | Quality control | #ff0000
+%%metro file: BCL_IN | BCL
+%%metro file: FASTQ_IN | FASTQ
+%%metro line_order: span
+%%metro compact_offsets: true
+
+graph TD
+    BCL_IN[]
+    FASTQ_IN[]
+    MULTIQC_LIBRARY
+    MULTIQC_SAV
+
+    subgraph Demultiplexing
+        BCL_IN -->|demux| BCLCONVERT
+        BCL_IN -->|qc| MULTIQC_SAV
+        BCLCONVERT -->|qc| MULTIQC_SAV
+    end
+
+    subgraph FASTQ_QC
+        FASTQ_IN -->|qc| FALCO
+        FASTQ_IN -->|qc| FASTP
+        BCLCONVERT -->|qc| FALCO
+        BCLCONVERT -->|qc| FASTP
+        FALCO -->|qc| MULTIQC_LIBRARY
+        FASTP -->|qc| MULTIQC_LIBRARY
+    end
+
+    _CRAM
+
+    subgraph Alignment_and_Postprocessing
+        FASTQ_IN -->|alignment| ALIGN
+        BCLCONVERT -->|alignment| ALIGN
+
+        ALIGN -->|alignment| MARKDUP
+        MARKDUP -->|alignment| _CRAM
+    end
+
+    _QC_COLLECT
+
+    subgraph COVERAGE_QC
+        _CRAM -->|qc| MOSDEPTH
+        _CRAM -->|qc| SAMTOOLS_COV
+        MOSDEPTH -->|qc| _QC_COLLECT
+        SAMTOOLS_COV -->|qc| _QC_COLLECT
+    end
+
+    subgraph BAM_QC
+        _CRAM -->|qc| SAMTOOLS_QC
+        _CRAM -->|qc| PICARD
+        SAMTOOLS_QC -->|qc| _QC_COLLECT
+        PICARD -->|qc| _QC_COLLECT
+    end
+
+    _QC_COLLECT -->|qc| MULTIQC_LIBRARY
diff --git a/docs/images/metro_map_light.svg b/docs/images/metro_map_light.svg
new file mode 100644
index 00000000..621e5caa
--- /dev/null
+++ b/docs/images/metro_map_light.svg
@@ -0,0 +1,264 @@
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+<text x="53.0" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">demultiplexing</text>
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+<text x="312.5" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">fastq_qc</text>
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+<text x="660.5" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_dna</text>
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+<text x="660.5" y="342.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_rna</text>
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+<text x="886.0" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_postprocessing</text>
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" />
+<path d="M371.5869980879541,908.0 L395.5869980879541,908.0" stroke="#4CAF50" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="908.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Main</text>
+<path d="M371.5869980879541,932.0 L395.5869980879541,932.0" stroke="#FF9800" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="932.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">RNA Alignment</text>
+<path d="M371.5869980879541,956.0 L395.5869980879541,956.0" stroke="#ff1e00ff" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="956.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">DNA Alignment</text>
+<path d="M371.5869980879541,980.0 L395.5869980879541,980.0" stroke="#9C27B0" stroke-width="3.0" stroke-linecap="round" />
+<text x="407.5869980879541" y="980.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">Alignment Postprocessing</text>
+<path d="M371.5869980879541,1004.0 L395.5869980879541,1004.0" stroke="#2196F3" stroke-width="3.0" stroke-linecap="round" />
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+<text x="1959.0" y="1044.0" font-size="8" fill="rgba(150, 150, 150, 0.6)" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" text-anchor="end">created with nf-metro v0.4.4</text>
+</svg>

From 4a6fa7b777512626f38340bcd0ca129ec032eb85 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 11:07:19 +0100
Subject: [PATCH 22/24] lint and spell check markdown

---
 CHANGELOG.md       |  2 +-
 README.md          |  4 ++--
 docs/output.md     |  8 +++----
 docs/parameters.md |  8 +++----
 docs/usage.md      | 57 ++++------------------------------------------
 5 files changed, 15 insertions(+), 64 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c062310d..34cc5393 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -50,7 +50,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## v2.0.2
 
-- Drop unsed params
+- Drop unused params
 - Set aligner to `star` for RNA-seq
 - Finetune resources
 - Fix some bugs for different input tags
diff --git a/README.md b/README.md
index 408d1a51..83b6f5e5 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ It also performs basic QC and coverage analysis.
 
 The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.
 
-Steps inlcude:
+Steps include:
 
 - Demultiplexing using [`BCLconvert`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html)
 - Run QC using [`MultiQC SAV`](https://github.com/MultiQC/MultiQC_SAV)
@@ -26,7 +26,7 @@ Steps inlcude:
 - Alignment using either [`bwa`](https://github.com/lh3/bwa), [`bwa-mem2`](https://github.com/bwa-mem2/bwa-mem2), [`bowtie2`](https://github.com/BenLangmead/bowtie2), [`dragmap`](https://github.com/Illumina/DRAGMAP), [`snap`](https://github.com/amplab/snap) or [`strobe`](https://github.com/ksahlin/strobealign) for DNA-seq and [`STAR`](https://github.com/alexdobin/STAR) for RNA-seq
 - Duplicate marking using [`bamsormadup`](https://gitlab.com/german.tischler/biobambam2) or [`samtools markdup`](http://www.htslib.org/doc/samtools-markdup.html)
 - Coverage analysis using [`mosdepth`](https://github.com/brentp/mosdepth) and [`samtools coverage`](http://www.htslib.org/doc/samtools-coverage.html)
-- Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollecHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)
+- Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollectHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)
 - QC aggregation using [`multiqc`](https://multiqc.info/)
 
 ![metro map](docs/images/metro_map.png)
diff --git a/docs/output.md b/docs/output.md
index ed562aa8..119dd95e 100644
--- a/docs/output.md
+++ b/docs/output.md
@@ -17,19 +17,19 @@ A separate directory will be created in the output directory for each sample con
 
 - `SAMPLE`
   - `SAMPLE.CollectHsMetrics.coverage_metrics`: The coverage metrics calculated by `picard CollectHsMetrics`
-  - `SAMPLE.CollectMultipleMetrics.alignment_summary_metrics`: The alignment summary metrics calculated by `picard CollectMultipleMEtrics`
+  - `SAMPLE.CollectMultipleMetrics.alignment_summary_metrics`: The alignment summary metrics calculated by `picard CollectMultipleMetrics`
   - `SAMPLE.CollectMultipleMetrics.base_distribution_by_cycle_metrics`: The base distribution by cycle metrics calculated by `picard CollectMultipleMetrics`
   - `SAMPLE.CollectMultipleMetrics.base_distribution_by_cycle.pdf`: PDF file containing the base distribution by cycle metrics
   - `SAMPLE.CollectMultipleMetrics.quality_by_cycle_metrics`: The quality by cycle metrics calculated by `picard CollectMultipleMetrics`
   - `SAMPLE.CollectMultipleMetrics.quality_by_cycle.pdf`: PDF file containing the quality by cycle metrics
   - `SAMPLE.CollectMultipleMetrics.quality_distribution_metrics`: The quality by distribution metrics calculated by `picard CollectMultipleMetrics`
   - `SAMPLE.CollectMultipleMetrics.quality_distribution.pdf`: PDF file containing the quality distribution metrics
-  - `SAMPLE.CollectMultipleMetrics.read_length_histogram.pdf`: A histogram detailing the read lenghts made with `picard CollectMultipleMetrics`
+  - `SAMPLE.CollectMultipleMetrics.read_length_histogram.pdf`: A histogram detailing the read lengths made with `picard CollectMultipleMetrics`
   - `SAMPLE.coverage.txt`: The coverage metrics calculated by `samtools coverage`
   - `SAMPLE.cram`: The CRAM file generated by the aligner
   - `SAMPLE.cram.crai`: The index of the CRAM file
   - `SAMPLE.cram.md5`: The md5sum of the CRAM file
-  - `SAMPLE.duplicate_metrics.txt`: The duplicate metrics calculated by `samtools mardup`
+  - `SAMPLE.duplicate_metrics.txt`: The duplicate metrics calculated by `samtools markdup`
   - `SAMPLE.fastp.html`: The HTML file visualising the metrics calculated by `fastp`
   - `SAMPLE.fastp.json`: The JSON file containing the metrics calculated by `fastp`
   - `SAMPLE.flagstat`: The quality control metrics calculated by `samtools flagstat`
@@ -82,7 +82,7 @@ Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQ
 
 - `pipeline_info/`
   - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.mmd`.
-  - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameter's are used when running the pipeline.
+  - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameters are used when running the pipeline.
 
 </details>
 
diff --git a/docs/parameters.md b/docs/parameters.md
index 700a2292..0f7220fc 100644
--- a/docs/parameters.md
+++ b/docs/parameters.md
@@ -16,10 +16,10 @@ Define where the pipeline should find input data and save output data.
 
 ## Pipeline options
 
-| Parameter     | Description                                                                                                                                                                                                                                                                                                                                                                                                        | Type      | Default   | Required | Hidden |
-| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------- | --------- | -------- | ------ |
-| `split_fastq` | Specify how many reads each split of a FastQ file contains. Set 0 to turn off splitting at all. <details><summary>Help</summary><small>Use the the tool FastP to split FASTQ file by number of reads. This parallelizes across fastq file shards speeding up mapping. Note although the minimum value is 250 reads, if you have fewer than 250 reads a single FASTQ shard will still be created.</small></details> | `integer` | 100000000 |          |        |
-| `genelists`   | Directory containing gene list bed files for granular coverage analysis                                                                                                                                                                                                                                                                                                                                            | `string`  | None      |          |        |
+| Parameter     | Description                                                                                                                                                                                                                                                                                                                                                                                                    | Type      | Default   | Required | Hidden |
+| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- | -------- | ------ |
+| `split_fastq` | Specify how many reads each split of a FastQ file contains. Set 0 to turn off splitting at all. <details><summary>Help</summary><small>Use the tool FastP to split FASTQ file by number of reads. This parallelizes across fastq file shards speeding up mapping. Note although the minimum value is 250 reads, if you have fewer than 250 reads a single FASTQ shard will still be created.</small></details> | `integer` | 100000000 |          |        |
+| `genelists`   | Directory containing gene list bed files for granular coverage analysis                                                                                                                                                                                                                                                                                                                                        | `string`  | None      |          |        |
 
 ## Institutional config options
 
diff --git a/docs/usage.md b/docs/usage.md
index 823d41e2..f59c809a 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -42,7 +42,6 @@ A `fastq` samplesheet file consisting of paired-end data may look something like
 
 Following table shows the fields that are used by the `fastq` samplesheet:
 
-<<<<<<< feat/nf-metro
 | Column                   | Description                                                                                                                                              | Required                                        |
 | ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- |
 | `id`                     | Unique sample identifier                                                                                                                                 | :heavy_check_mark:                              |
@@ -65,19 +64,6 @@ Following table shows the fields that are used by the `fastq` samplesheet:
 | `sample_type`            | Sample type (e.g., `DNA`, `RNA`)                                                                                                                         | :x:                                             |
 | `fastq_1`                | FastQ file for reads 1 must be provided, cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                                           | :heavy_check_mark:                              |
 | `fastq_2`                | FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                                                             | :x:                                             |
-=======
-| Column       | Description                                                                                                                                  | Required                                        |
-| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- |
-| `fastq_1`    | FastQ file for reads 1 must be provided, cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                               | :heavy_check_mark:                              |
-| `fastq_2`    | FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'                                                 | :x:                                             |
-| `samplename` | The sample name corresponding to the sample in the Fastq file(s)                                                                             | :heavy_check_mark:                              |
-| `genome`     | The genome build to use for the analysis. Currently supports GRCh38, GRCm39 and GRCz11                                                       | :heavy_check_mark: (unless `organism` is given) |
-| `organism`   | Full name of the organism. Currently supports "Homo sapiens", "Mus musculus" and "Danio rerio"                                               | :heavy_check_mark: (unless `genome` is given)   |
-| `library`    | Sample library name                                                                                                                          | :x:                                             |
-| `tag`        | The tag used by the sample. Can be one of WES, WGS or coPGT-M                                                                                | :heavy_check_mark:                              |
-| `roi`        | The path to a BED file containing <b>R</b>egions <b>O</b>f <b>I</b>nterest for coverage analysis                                             | :x:                                             |
-| `aligner`    | The aligner to use for this sample. Can be one of these: bowtie2, bwamem, bwamem2, dragmap, strobe and snap. set to `false` to output fastq. | :x:                                             |
->>>>>>> dev
 
 An [example samplesheet](../tests/inputs/test.yml) has been provided with the pipeline.
 
@@ -108,7 +94,6 @@ An [example samplesheet](../tests/inputs/test.yml) has been provided with the pi
 
 A `flowcell` sample info JSON/YML file consisting for one sequencing run may look something like the one below.
 
-<<<<<<< feat/nf-metro
 ```yml
 - id: DNA1_L001
   samplename: DNA_paired1
@@ -127,40 +112,6 @@ A `flowcell` sample info JSON/YML file consisting for one sequencing run may loo
   roi: null
   tag: WES
   sample_type: DNA
-=======
-```json title="sample_info.json"
-{
-  "samplename": "Sample1",
-  "library": "test",
-  "organism": "Homo sapiens",
-  "tag": "WES"
-}
-```
-
-Following table shows the fields that are used by the `flowcell` samplesheet:
-
-| Column          | Description                                                                                                                                  | Required           |
-| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ |
-| `samplename`    | The sample name                                                                                                                              | :heavy_check_mark: |
-| `library`       | The library name                                                                                                                             | :x:                |
-| `tag`           | Sample tag. Has to be one of these: WES, WGS, coPGT-M                                                                                        | :heavy_check_mark: |
-| `organism`      | The organism of the sample. Has to be one of these: "Homo sapiens", "Mus musculus" or "Danio rerio"                                          | :heavy_check_mark: |
-| `vivar_project` | The vivar project name (currently not used by the pipeline)                                                                                  | :x:                |
-| `binsize`       | The binsize for CNV analysis (currently not used by the pipeline)                                                                            | :x:                |
-| `panels`        | A list of panels for coverage analysis                                                                                                       | :x:                |
-| `roi`           | Region of interest BED file for coverage analysis                                                                                            | :x:                |
-| `aligner`       | The aligner to use for this sample. Can be one of these: bowtie2, bwamem, bwamem2, dragmap, strobe and snap. Set to `false` to output fastq. | :x:                |
-
-### Multiple runs of the same sample
-
-The `sample` identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:
-
-```csv title="samplesheet.csv"
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
->>>>>>> dev
 ```
 
 ## Running the pipeline
@@ -222,7 +173,7 @@ First, go to the [nf-cmgg/preprocessing releases page](https://github.com/nf-cmg
 
 This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.
 
-To further assist in reproducbility, you can use share and re-use [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
+To further assist in reproducibility, you can use share and re-use [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
 
 :::tip
 If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
@@ -245,7 +196,7 @@ The pipeline also dynamically loads configurations from [https://github.com/nf-c
 Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important!
 They are loaded in sequence, so later profiles can overwrite earlier profiles.
 
-If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer enviroment.
+If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer environment.
 
 - `debug`
   - A generic profile with settings to help with debugging the pipeline. It will use more verbose logging.
@@ -289,7 +240,7 @@ To change the resource requests, please see the [max resources](https://nf-co.re
 
 ### Custom Containers
 
-In some cases you may wish to change which container a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However in some cases the pipeline specified version maybe out of date.
+In some cases you may wish to change which container a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However in some cases the pipeline specified version may be out of date.
 
 To use a different container from the default container specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/usage/configuration#updating-tool-versions) section of the nf-core website.
 
@@ -314,7 +265,7 @@ Nextflow handles job submissions and supervises the running jobs. The Nextflow p
 The Nextflow `-bg` flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.
 
 Alternatively, you can use `screen` / `tmux` or similar tool to create a detached session which you can log back into at a later time.
-Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).
+Some HPC setups also allow you to run nextflow within a cluster job submitted to your job scheduler (from where it submits more jobs).
 
 ## Nextflow memory requirements
 

From 4b82db83740488818ab929801dc9f28e13e12db2 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 11:41:33 +0100
Subject: [PATCH 23/24] fix metro maps

---
 README.md                       |   1 -
 docs/images/metro_map_dark.md   |  45 ++++
 docs/images/metro_map_dark.mmd  |  58 ------
 docs/images/metro_map_dark.svg  | 353 +++++++++-----------------------
 docs/images/metro_map_light.md  |  45 ++++
 docs/images/metro_map_light.mmd |  58 ------
 docs/images/metro_map_light.svg | 353 +++++++++-----------------------
 7 files changed, 278 insertions(+), 635 deletions(-)
 create mode 100644 docs/images/metro_map_dark.md
 delete mode 100644 docs/images/metro_map_dark.mmd
 create mode 100644 docs/images/metro_map_light.md
 delete mode 100644 docs/images/metro_map_light.mmd

diff --git a/README.md b/README.md
index 83b6f5e5..2d6849f9 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,6 @@ Steps include:
 - Alignment QC using [`samtools flagstat`](http://www.htslib.org/doc/samtools-flagstat.html), [`samtools stats`](http://www.htslib.org/doc/samtools-stats.html), [`samtools idxstats`](http://www.htslib.org/doc/samtools-idxstats.html) and [`picard CollectHsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectHsMetrics), [`picard CollectWgsMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectWgsMetrics), [`picard CollectMultipleMetrics`](https://broadinstitute.github.io/picard/command-line-overview.html#CollectMultipleMetrics)
 - QC aggregation using [`multiqc`](https://multiqc.info/)
 
-![metro map](docs/images/metro_map.png)
 <picture>
 
   <source media="(prefers-color-scheme: dark)" srcset="docs/images/metro_map_dark.svg">
diff --git a/docs/images/metro_map_dark.md b/docs/images/metro_map_dark.md
new file mode 100644
index 00000000..65349f39
--- /dev/null
+++ b/docs/images/metro_map_dark.md
@@ -0,0 +1,45 @@
+```mermaid
+%%metro logo: ./nf-cmgg-preprocessing_logo_dark.png
+%%metro style: dark
+%%metro line: main | Alignment and Postprocessing | #00ff00
+%%metro line: qc | Quality control | #ff0000
+%%metro file: BCL_IN | BCL
+%%metro file: FASTQ_IN | FASTQ
+%%metro file: CRAM_OUT | CRAM
+%%metro file: MULTIQC_LIBRARY | HTML
+%%metro file: MULTIQC_SAV | HTML
+%%metro compact_offsets: true
+
+graph TD
+
+    BCL_IN[]
+    FASTQ_IN[]
+    MULTIQC_SAV[]
+    CRAM_OUT[]
+    MULTIQC_LIBRARY[]
+
+    BCL_IN -->|main | BCLCONVERT
+    BCL_IN -->|qc| MULTIQC_SAV
+    BCLCONVERT -->|qc| MULTIQC_SAV
+
+    FASTQ_IN[]
+    FASTQ_IN -->|qc,main| FASTP
+    BCLCONVERT -->|qc| FALCO
+    BCLCONVERT -->|qc,main| FASTP
+    FALCO -->|qc| MULTIQC_LIBRARY
+    FASTP -->|qc| MULTIQC_LIBRARY
+
+    FASTP -->|main| ALIGN
+    ALIGN -->|main| MARKDUP
+    MARKDUP -->|main| CRAM_OUT
+
+    CRAM_OUT -->|qc| MOSDEPTH
+    CRAM_OUT -->|qc| SAMTOOLS_COV
+    MOSDEPTH -->|qc| MULTIQC_LIBRARY
+    SAMTOOLS_COV -->|qc| MULTIQC_LIBRARY
+
+    CRAM_OUT -->|qc| SAMTOOLS_QC
+    CRAM_OUT -->|qc| PICARD
+    SAMTOOLS_QC -->|qc| MULTIQC_LIBRARY
+    PICARD -->|qc| MULTIQC_LIBRARY
+```
diff --git a/docs/images/metro_map_dark.mmd b/docs/images/metro_map_dark.mmd
deleted file mode 100644
index 4c4458e3..00000000
--- a/docs/images/metro_map_dark.mmd
+++ /dev/null
@@ -1,58 +0,0 @@
-%%metro title: nf-cmgg/preprocessing
-%%metro style: nf-core
-%%metro line: demux | Demultiplexing | #0000ff
-%%metro line: alignment | Alignment and Postprocessing | #00ff00
-%%metro line: qc | Quality control | #ff0000
-%%metro file: BCL_IN | BCL
-%%metro file: FASTQ_IN | FASTQ
-%%metro line_order: span
-%%metro compact_offsets: true
-
-graph TD
-    BCL_IN[]
-    FASTQ_IN[]
-    MULTIQC_LIBRARY
-    MULTIQC_SAV
-
-    subgraph Demultiplexing
-        BCL_IN -->|demux| BCLCONVERT
-        BCL_IN -->|qc| MULTIQC_SAV
-        BCLCONVERT -->|qc| MULTIQC_SAV
-    end
-
-    subgraph FASTQ_QC
-        FASTQ_IN -->|qc| FALCO
-        FASTQ_IN -->|qc| FASTP
-        BCLCONVERT -->|qc| FALCO
-        BCLCONVERT -->|qc| FASTP
-        FALCO -->|qc| MULTIQC_LIBRARY
-        FASTP -->|qc| MULTIQC_LIBRARY
-    end
-
-    _CRAM
-
-    subgraph Alignment_and_Postprocessing
-        FASTQ_IN -->|alignment| ALIGN
-        BCLCONVERT -->|alignment| ALIGN
-
-        ALIGN -->|alignment| MARKDUP
-        MARKDUP -->|alignment| _CRAM
-    end
-
-    _QC_COLLECT
-
-    subgraph COVERAGE_QC
-        _CRAM -->|qc| MOSDEPTH
-        _CRAM -->|qc| SAMTOOLS_COV
-        MOSDEPTH -->|qc| _QC_COLLECT
-        SAMTOOLS_COV -->|qc| _QC_COLLECT
-    end
-
-    subgraph BAM_QC
-        _CRAM -->|qc| SAMTOOLS_QC
-        _CRAM -->|qc| PICARD
-        SAMTOOLS_QC -->|qc| _QC_COLLECT
-        PICARD -->|qc| _QC_COLLECT
-    end
-
-    _QC_COLLECT -->|qc| MULTIQC_LIBRARY
diff --git a/docs/images/metro_map_dark.svg b/docs/images/metro_map_dark.svg
index 621e5caa..50767fc6 100644
--- a/docs/images/metro_map_dark.svg
+++ b/docs/images/metro_map_dark.svg
@@ -1,264 +1,99 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"
-     width="1989" height="1052" viewBox="0 0 1989 1052">
+     width="911" height="526" viewBox="0 0 911 526">
 <defs>
 </defs>
-<rect x="0" y="0" width="1989" height="1052" fill="#2b2b2b" />
-<rect x="30.0" y="85.0" width="209.5" height="70.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="39.0" cy="72.0" r="9" fill="#333333" />
-<text x="39.0" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">1</text>
-<text x="53.0" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">demultiplexing</text>
-<rect x="289.5" y="85.0" width="268.0" height="150.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="298.5" cy="72.0" r="9" fill="#333333" />
-<text x="298.5" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">2</text>
-<text x="312.5" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">fastq_qc</text>
-<rect x="637.5" y="85.0" width="185.5" height="242.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="646.5" cy="72.0" r="9" fill="#333333" />
-<text x="646.5" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">3</text>
-<text x="660.5" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_dna</text>
-<rect x="637.5" y="355.0" width="136.0" height="70.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="646.5" cy="342.0" r="9" fill="#333333" />
-<text x="646.5" y="342.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">4</text>
-<text x="660.5" y="342.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_rna</text>
-<rect x="863.0" y="85.0" width="509.5" height="150.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="872.0" cy="72.0" r="9" fill="#333333" />
-<text x="872.0" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">5</text>
-<text x="886.0" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_postprocessing</text>
-<rect x="1422.5" y="105.0" width="506.5" height="340.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="1431.5" cy="92.0" r="9" fill="#333333" />
-<text x="1431.5" y="92.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">6</text>
-<text x="1445.5" y="92.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_qc</text>
-<rect x="1647.5" y="375.0" width="281.5" height="134.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="1656.5" cy="362.0" r="9" fill="#333333" />
-<text x="1656.5" y="362.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">7</text>
-<text x="1670.5" y="362.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_qc_wgs</text>
-<rect x="1656.5" y="509.0" width="272.5" height="134.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="1665.5" cy="496.0" r="9" fill="#333333" />
-<text x="1665.5" y="496.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">8</text>
-<text x="1679.5" y="496.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_qc_targetted</text>
-<rect x="1590.5" y="643.0" width="338.5" height="170.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="1599.5" cy="630.0" r="9" fill="#333333" />
-<text x="1599.5" y="630.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">9</text>
-<text x="1613.5" y="630.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">coverage_qc</text>
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-<text x="1715.0" y="650.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">10</text>
-<text x="1729.0" y="650.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">reporting</text>
-<path d="M1819.0,813.0 L1744.0,663.0" stroke="#2196F3" stroke-width="3.0" stroke-linecap="round" />
-<path d="M1819.0,813.0 L1744.0,663.0" stroke="#2196F3" stroke-width="3.0" stroke-linecap="round" />
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/>
+<path d="M211.03479923518165,454.0 L235.03479923518165,454.0" stroke="#00ff00" stroke-width="3.0" stroke-linecap="round" />
+<text x="247.03479923518165" y="454.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dy="0.3em">Alignment and Postprocessing</text>
+<path d="M211.03479923518165,478.0 L235.03479923518165,478.0" stroke="#ff0000" stroke-width="3.0" stroke-linecap="round" />
+<text x="247.03479923518165" y="478.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dy="0.3em">Quality control</text>
+<text x="881.0" y="518.0" font-size="8" fill="rgba(150, 150, 150, 0.6)" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" text-anchor="end">created with nf-metro v0.5.4</text>
 </svg>
diff --git a/docs/images/metro_map_light.md b/docs/images/metro_map_light.md
new file mode 100644
index 00000000..5cd6a5b0
--- /dev/null
+++ b/docs/images/metro_map_light.md
@@ -0,0 +1,45 @@
+```mermaid
+%%metro logo: ./nf-cmgg-preprocessing_logo_light.png
+%%metro style: light
+%%metro line: main | Alignment and Postprocessing | #00ff00
+%%metro line: qc | Quality control | #ff0000
+%%metro file: BCL_IN | BCL
+%%metro file: FASTQ_IN | FASTQ
+%%metro file: CRAM_OUT | CRAM
+%%metro file: MULTIQC_LIBRARY | HTML
+%%metro file: MULTIQC_SAV | HTML
+%%metro compact_offsets: true
+
+graph TD
+
+    BCL_IN[]
+    FASTQ_IN[]
+    MULTIQC_SAV[]
+    CRAM_OUT[]
+    MULTIQC_LIBRARY[]
+
+    BCL_IN -->|main | BCLCONVERT
+    BCL_IN -->|qc| MULTIQC_SAV
+    BCLCONVERT -->|qc| MULTIQC_SAV
+
+    FASTQ_IN[]
+    FASTQ_IN -->|qc,main| FASTP
+    BCLCONVERT -->|qc| FALCO
+    BCLCONVERT -->|qc,main| FASTP
+    FALCO -->|qc| MULTIQC_LIBRARY
+    FASTP -->|qc| MULTIQC_LIBRARY
+
+    FASTP -->|main| ALIGN
+    ALIGN -->|main| MARKDUP
+    MARKDUP -->|main| CRAM_OUT
+
+    CRAM_OUT -->|qc| MOSDEPTH
+    CRAM_OUT -->|qc| SAMTOOLS_COV
+    MOSDEPTH -->|qc| MULTIQC_LIBRARY
+    SAMTOOLS_COV -->|qc| MULTIQC_LIBRARY
+
+    CRAM_OUT -->|qc| SAMTOOLS_QC
+    CRAM_OUT -->|qc| PICARD
+    SAMTOOLS_QC -->|qc| MULTIQC_LIBRARY
+    PICARD -->|qc| MULTIQC_LIBRARY
+```
diff --git a/docs/images/metro_map_light.mmd b/docs/images/metro_map_light.mmd
deleted file mode 100644
index 4c4458e3..00000000
--- a/docs/images/metro_map_light.mmd
+++ /dev/null
@@ -1,58 +0,0 @@
-%%metro title: nf-cmgg/preprocessing
-%%metro style: nf-core
-%%metro line: demux | Demultiplexing | #0000ff
-%%metro line: alignment | Alignment and Postprocessing | #00ff00
-%%metro line: qc | Quality control | #ff0000
-%%metro file: BCL_IN | BCL
-%%metro file: FASTQ_IN | FASTQ
-%%metro line_order: span
-%%metro compact_offsets: true
-
-graph TD
-    BCL_IN[]
-    FASTQ_IN[]
-    MULTIQC_LIBRARY
-    MULTIQC_SAV
-
-    subgraph Demultiplexing
-        BCL_IN -->|demux| BCLCONVERT
-        BCL_IN -->|qc| MULTIQC_SAV
-        BCLCONVERT -->|qc| MULTIQC_SAV
-    end
-
-    subgraph FASTQ_QC
-        FASTQ_IN -->|qc| FALCO
-        FASTQ_IN -->|qc| FASTP
-        BCLCONVERT -->|qc| FALCO
-        BCLCONVERT -->|qc| FASTP
-        FALCO -->|qc| MULTIQC_LIBRARY
-        FASTP -->|qc| MULTIQC_LIBRARY
-    end
-
-    _CRAM
-
-    subgraph Alignment_and_Postprocessing
-        FASTQ_IN -->|alignment| ALIGN
-        BCLCONVERT -->|alignment| ALIGN
-
-        ALIGN -->|alignment| MARKDUP
-        MARKDUP -->|alignment| _CRAM
-    end
-
-    _QC_COLLECT
-
-    subgraph COVERAGE_QC
-        _CRAM -->|qc| MOSDEPTH
-        _CRAM -->|qc| SAMTOOLS_COV
-        MOSDEPTH -->|qc| _QC_COLLECT
-        SAMTOOLS_COV -->|qc| _QC_COLLECT
-    end
-
-    subgraph BAM_QC
-        _CRAM -->|qc| SAMTOOLS_QC
-        _CRAM -->|qc| PICARD
-        SAMTOOLS_QC -->|qc| _QC_COLLECT
-        PICARD -->|qc| _QC_COLLECT
-    end
-
-    _QC_COLLECT -->|qc| MULTIQC_LIBRARY
diff --git a/docs/images/metro_map_light.svg b/docs/images/metro_map_light.svg
index 621e5caa..49817c64 100644
--- a/docs/images/metro_map_light.svg
+++ b/docs/images/metro_map_light.svg
@@ -1,264 +1,99 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"
-     width="1989" height="1052" viewBox="0 0 1989 1052">
+     width="911" height="526" viewBox="0 0 911 526">
 <defs>
 </defs>
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-<rect x="30.0" y="85.0" width="209.5" height="70.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="39.0" cy="72.0" r="9" fill="#333333" />
-<text x="39.0" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">1</text>
-<text x="53.0" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">demultiplexing</text>
-<rect x="289.5" y="85.0" width="268.0" height="150.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="298.5" cy="72.0" r="9" fill="#333333" />
-<text x="298.5" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">2</text>
-<text x="312.5" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">fastq_qc</text>
-<rect x="637.5" y="85.0" width="185.5" height="242.0" rx="8" ry="8" fill="rgba(255, 255, 255, 0.06)" stroke="rgba(255, 255, 255, 0.2)" stroke-width="1.0" />
-<circle cx="646.5" cy="72.0" r="9" fill="#333333" />
-<text x="646.5" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">3</text>
-<text x="660.5" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_dna</text>
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-<text x="646.5" y="342.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">4</text>
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-<text x="872.0" y="72.0" font-size="9" fill="#ffffff" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" font-weight="bold" text-anchor="middle" dominant-baseline="central">5</text>
-<text x="886.0" y="72.0" font-size="13.0" fill="#aaaaaa" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dominant-baseline="central">alignment_postprocessing</text>
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+<path d="M211.2781609195402,454.0 L235.2781609195402,454.0" stroke="#00ff00" stroke-width="3.0" stroke-linecap="round" />
+<text x="247.2781609195402" y="454.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dy="0.3em">Alignment and Postprocessing</text>
+<path d="M211.2781609195402,478.0 L235.2781609195402,478.0" stroke="#ff0000" stroke-width="3.0" stroke-linecap="round" />
+<text x="247.2781609195402" y="478.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dy="0.3em">Quality control</text>
+<text x="881.0" y="518.0" font-size="8" fill="rgba(150, 150, 150, 0.6)" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" text-anchor="end">created with nf-metro v0.5.4</text>
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From 108fe63432199af84652ad94c1b235526ef97e56 Mon Sep 17 00:00:00 2001
From: Matthias De Smet <11850640+matthdsm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 11:51:06 +0100
Subject: [PATCH 24/24] more metro fixes

---
 docs/images/metro_map_dark.svg                |   2 +-
 docs/images/metro_map_light.svg               | 173 +++++++++---------
 .../nf-cmgg-preprocessing_logo_dark.png       | Bin 79933 -> 65764 bytes
 .../nf-cmgg-preprocessing_logo_dark.svg       |   2 +-
 .../nf-cmgg-preprocessing_logo_light.png      | Bin 79022 -> 65952 bytes
 .../nf-cmgg-preprocessing_logo_light.svg      |  14 +-
 6 files changed, 98 insertions(+), 93 deletions(-)

diff --git a/docs/images/metro_map_dark.svg b/docs/images/metro_map_dark.svg
index 50767fc6..0f452bc5 100644
--- a/docs/images/metro_map_dark.svg
+++ b/docs/images/metro_map_dark.svg
@@ -90,7 +90,7 @@
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d+YXv7W/fqhiO6KhgTAFABFLGBIoRB8hxzDY87R+U4rUQAAAAwIOXz+Z0kDar0vr97/O0XCpafb9Kn1/hg9xdc9p3M9L0f1rSYBjiOm5No3HDhKfLEmTINKX4DXyK3s+qDjp+/fsn4zsoHLEF6cbKxdfn+suA8Rb61zMu5y/z5QDovmau97d0rRi2tWEYAQEVRxAZ6aItbvr+eef57MQ4PrzwzTmIDAABgoNq10hs+/kaHJv7qqVNN+sQaHuou/SlI+qGZS8b+s9I5emrID+/cIBEGsyXtIykoarHL3fTHSNExS2fs+2ymKl4IudVPXriVtXZOlQVjJE+U0ST1PbkuTER+9fuzxsbWoxwA0DMUsYEeCiyc4FJd3DkqKUiEFLEBAAAwIEVRVLGhji7pf55/T8ff/HS+EPmaCtgFM/3aZZPbLtnn/YoEKFbag8bMoq8p9MslbVXCDsvcdHF7wS/QrH2zlY5XimGT5jcUkguPk+v0EgdSrtRu0i/CVPSTZRfv81bFAgIAehVFbKCHXJoQd4bK8nee3+/nb8SdAgAAAKg0d0+EYbhzRfaSdNfT72ry3Gf03rJwDa+hLZSiabWJxLlvTx29rBLXL9qE6wc1tS481s3Pkayp+A3sGRWiKe2zxt5V+XAlSC9ONr7X+bVCws6T2xelkluHZN21SFH+/MysfR+S4upPDgAoBUVsoAdG3jpxC6mwTdw5KopWIgAAABigwjDcXlJjufu4S3c/u0ST5z6r95aFq3+wqUsenZ1Zvt6MzOwd1vDg3lF/0rzhQZS8wN0Ol5QqbrWFcs0NlDi9ddber/ZKwCKt0zJvw3xr1xlK2GGS6kvqaOJySX+TovOHNQ++85X0qKod2jh00vxtg0Bfq0slro/tTRAAqFIUsYEecOXHWhnN1qqS67G4IwAAAAC9pCL9sP/w4vs6/jdPq61zjQXsjHtwfPsbmd9o7thCJa5drPofzNs6KCR/IWmH4ou91ubmZ7a3Dfov3VAFRd60Bw1tCw/Ou9Iy27KM09fvSbrQA7uufdo+72cqmbGChp10z7aFQjhZrm/I1JwNoy2VnvNDpcfn4s4GANWCIjbQAybbMe4MFcdJbAAAAAxQZrare3ndIv7+eoe+/+un1lzAlpYoCr7dPn2ve2TxtKhomHLHPqbgOknrF7vWpKci9yPam/76iKalo16IV5ThP7hrRNi2cJqkAyTVlDZP0iPJblbBz87MGvuyqmIo5Uc1nDBvS7PkqYVC/huSmlaem3LTcY2tQ8PMxIdPV0yn+gGg2lDEBnrmC3EHqLSgEFHEBgAAwIDj7haG4VfK2ePJNzt0yH/9TUuWrvEg7IuB6xutM0Y/rhnlXLFEExYPamjsPMVkZ0iqLXJ13l2/SUR+fNusse29Ea8o6Tk1TZmG74Sev0iy4aVt4pGkRxXZaZlhf12sdPxF+Y8YNydRv/GwLSwKTzfpW5JqV1FjT8qspWHwu0vbJ159vmYfQyEbwFpvgPVHACpvxLyJgwfnC+2SEnFnkdTda0/6m0vvmStj8qWuICHzBrlSkq8n2Sck20Dyuo/ZJfPiAdc1y6rzRAIAAABQqmw2u7WZPVPq+uffWabDb/i7XlrSuYZH+sNR4N/puHSf50u9VjmG/PDODZK5YJqkg1V878P3JT8r0/Tg1fEXet3qJy/cKjC7QPL9VOrrLtdbki4dlAquqMp+0uPmJOo3HrJFUAgmy/wIyT7utdoHheY6ta157xlKW/UV5AGgD3ESG1iDwbloOwWxF7DbJV1rFs1peDv16CPHzO7RO/GbzpmwQTKV3NqlrQLpM27+Rbk+L+kxCtgAAAAYiMxst1LXvt7apRPmPN2DArbutdCP7Lh8n3+Weq1yNE9esF0U6hqZim176JIei+QndEwf84A0NtbXBBu1zKnriBYe7qZzJB9R4jY5Sbe5J85qn7nXC9XX97q7SJ8IdJxH+pZM6xZxnjDl0k8a2ha80y79SlXaFgUA+gJFbGBNzLeNOcEvojAx6eXxs4t+PvbK+BvekvSWpPtWfuzTc8bVhHV161YwHwAAAFBNShrqmOkMdeptz+nR19bQWcO1MMqnDu24fI/3SrlOuRpb7torigrXSdqoyKV5c/0mSARnZC4d/VpvZCtG48mLNmvPR9PMtI+VXJuwJ6Xox5mmpb+rxiGI67TM2zDvC4932XfdSyzSmwabgsuaJt/5WtuMff5Q4YgA0G9QxAbWwEwj4nq722TnvHDgtedVcs+nxs/NSXqjknsCAAAAVaToInYuH+mnC1/SPc++p9XNgzT5zz0XTem4ckxrOQFLkvagoW3B8fLCT2RqKHJ1Vq6z24KOy3Tp+DUeM+9V6XTQ1LbT4Z6PzjVp49I28U65ZuZrounLLt7nrcoGLN/wU/5YH2aXHpf36DhJm5R/E6w3uQVna9ycvTV3fKESGQGgv6GIDayBS5+I6cK/e+GgyhawAQAAgIFs+fLlG0n6ZDFr3KUb/vy6bvjLG4o+ptZopsjdbkjlBk1+98pRSysQtSgj0vMGL29beIHLjpFUU9Ri01tm9u22hr/8T9z9r9dpmbdhvi1xicsOkjxVwhYu+YOKEsdnhu31WLX1id6o5YG6pWo/JMy1nyXZZpKVO4cskuxxM80cvCy8OUMBG8BajCI2sAbm9gm3vj+LHQWFs/r8ogAAAEA/lkgkdi92zf+88L7OufPFjy1gpxKmTZoH3fjoq0O/r9k79Gg2TSWt0zJvw+VtyStd2q+ohS536U+BdFjbtL1f6Z10PZRenGxq69w37zZd0idLau3sapX0s0zz0hlV1zpkwuJB9Y3L9+jwtvMk216yItper1JOrkfNdHHb8uHzNHuHsK1CUQGgv6KIDayBW8kDRkpmrsdfPvCGJ/r6ugAAAEB/ZmZFtRJ54d3l+sHNT6nwMRXsVCLQxK9slH2zbfBxj561WZ8XsJtb7vxs3oMbJH2hyKVZmW5I1CZPbb1wz1hnHdafNG94orXzR242UcWeIu+Wl/zeQPbD1hljHq90vrKkFyfr27q+GFjXWXLbW7KgzB1Dl/5qbtMzUXSHZo3NViQnAAwAFLGBNXBp/XLvASuaaW5fXxIAAAAYAHpcxG7rDHXyfz+rtztWfai3JhHopD021Q++uum9Q+tquyqWsIcaJ83fK4p0nazYAY7W5iqc1d607JpYTyy7W9OU+bt6Ppjups+Xtom9KkUXDEomfv721NHLKhuwHG5Dp9y5dSKTbZF0iFz1ZR69DiU9ILdZw5pr73wlParPf94AoNpRxAbWwFT00JTyr2n+l76+JgAAANCfufvwMAy36cljC5Hrknte0V/+ser5jDUJU3rfkTr6yxsrMPtDRYP2QMOUhYfJfIZczcWt9FfdNbG9+aG74+x/PWLivMHLTlx4glvwQ0nDStgikuv2qBCe0XHZvs9lFEN/x49Rf9K84VZYdKwpcYzcNyxzu0jSI3K7sCZsX/TuFeOXxnpsHgCqGEVsYM1q+/qCZnq6r68JAAAA9Ge5XO4rZj0bpLfgqSW68a+rHuRYkzCdu9+WmrDzRivP1vZdETu9ONnY1vVDyc+Sa3BRa02PeCH5rfaZe73QS+l6pGnKgk2Xus8y194qreaQURSdlhk2+AZV2YnkpikLD/O8zpBFW5XfOsSf8Mh+WjMod+eSi/bvqExCABi4KGIDa1ZK37ZyZJ7b/7/e7ONrAgAAAP1aT/th/+O9Tp017zl15gof+dygVKBz991S3915IwXdFezOZDL5cGWTrtq6xy0eGrZ2/sxlx8qUKGJpJNlvg5rExFj7X6c9qG+9a6wrusrMSjih7JFkd7vZD9pn7vNi5QOWKO1BY2b+F6TgAvdolMyCkluHuFyml2S6uF5Lf/H6zPGdlQ0LAAMXRWxgdeaMS0hK9fFV3+7j6wEAAAADwRqL2F1hQefMe15vtH10Xt6Q2oR+vM9IHf7/J7Al6c9m1ut9pYdMvn39XJC9XG7fKG6ld5r8oraC/UwX7h3bEMBhk+Y35NsW/djMJ0lFFeBXsLdNfkFb21tX6YYjquT0tVvz5Ds29tYFP3QLvieptoy+15GkVy2wy6wmcW3rhXtmaBsCAMWhiA2sxoi65lrlP3pCo5dV0cASAAAAoPq5+9AwDFc7PNBdmvPoW7rrmfc+8rkhtUn9eJ+ROmynEf9WpnT3+yud9cMaW+7YXFHiWrmPKnLpuxb5qW3/WvZzzR3f5y9aVmqevGC7vHS5yb9SwvJI0n2Sftg2feyjlU1WuuGn3F4fZhd+O7Lk6ZI+WdZmrrdkuj5R8CvenzXm9cokBIC1D0VsYDWSlqiV+vb5oFHEBgAAAIqSz+d30Rpe3/7jveX62aKXFfm/N8IelEroJ/ttqe/sOOIj52yDIOjVfthDJ83fVpFulukzRS10/UOu77XNGHuvLKahhxMfTjUMXnJIJP+pSRuVsMMyl12YLEQz3p81pr3i+UrUNHnRbmGucJ5MX1ZJp8pXsqVy/2XBCjOWTt/3mYoFBIC1FEVsYDVSOU+VfsdYaVxOERsAAAAoQhRFu65upmMhcp1x+/NasvTfO4PUJgNddOCndMgOn1jV0/4wmUz+pdJZV2o4cf7OFuk3khV30tf1WCR9q2PmmOc0s5fCrcHwU26vz+WWnGvu35dpUPE7+JMWBSdkZoy+L7Yi/IcMnrLwEzXSme7RdyUbWsZWBZfuUCG6oH2dMQ8qbVHFQgLAWowiNlBlTBbbrYAAAABAf2Rmu63u87997C0tfv7f24ikEoEuPmhrHfwfG3zcuZWHzGx5xUJ+QOPkRXsqim6StF6RSxdFyfyhHZfst6Q3cvVEY8sdm4e5xE0m30mmoKjFroKkGwqRTl86a+934yrC/5v04mRDJjve3M936ZOyMo4xmT3i0lntje33Kj2+13upA8DahCI2AAAAAKDfcveaMAy/+HGffzOT1fkLXlL0gfO+NQnTmXtvofFf2EDBx5zgdvfKtxJJp4OG1p2+LYsul9RQxMqCpGuHLM+f+Obs/XqlsL5G4+YkGjYcvLc8caOkdYpd7tKbMj+lvWnMr6vidLK7DTtx0baF1q6pMo1WGVMbJb0mt/MyTe03UrwGgN5BERsAAAAA0G/l8/kdJA1e1ecKkWvqPS/rrY7/ryvWJgOdttfmmrjrxkoEH1+3TCQSlR3qOPHqVGNmkyNkulTSkJ4uc1eXzH7aXoguyszeL1vRTD20/smLhnTmo5NNOl1SbZHLI8nvixT9oFp6QzefendjNGXB8QWz02SqL30nb3O366NUcMHSqaPfqVxCAMCHUcQGAAAAAPRnH9tK5KF/ZnTb39+RrxjmmEoEOnmPzfT93TZRcjUFbEmFRCLxQMUSjpuTaKwb+kO50pJSPV5nypj7DzNNg65XelS+YnmK0Dz57k268uEMk74uWXHtQ6Rlkk0flAx+9vbUsfHP/hk3J9G0ccNuUTZ/vsx2KWOnrMzuUCH4WfuMvR6tlr7eADCQFfsLCAPYVxeneVMDQFVw98Dd+TdpLba2f//dvSbuDPh4a/vP59qO73/1iaLoK6v6eGeuoGm/f0UdXd2138Ck43bbRMeuuYAtSY+bWVtFAqbn1DSNqE/LgrSKKWBLS6IoODQzfcy18RSw3Zpa5u8eWf53kh1QQgH7ZXN9O9P0l3Penjo69gL20Enz123YsP4ij/x2SaUWsF3yv8l1WHNj7aGZmaMfqcYCdnpxetBJi08aHncOAKgknoCtJba47YiNJdvBpG0UaaTMNpOsQfImdfdiq3+97bXakbceJUnLJHVKapeUkftLMnvBzF6ICv78kKG5vz0++hexPwkB0L+4+/r5fH5kFEWbBkGwqbtvKmlTScMlNbh7nZkNktQQhmFCknK5nCSFkpZKcknvSnrXzN5193+Z2dtRFD1nZs+nUqnnemv4EirL3QeHYbitpM9I+oy7f9bM1pXUuOJPQxiGqRXf/1Z1f/+XSspIet7MnjKzJ8IwfLquru6fMX0ZJXP3ZC6X+5Skz0r6nKTPmtkIdX/tTZLqP/D1Swjh1eUAACAASURBVFKXmS1x9yXu/qaZvSzpRTN7MplMPlKxIgtW/mx+1t23lPQpM9tS0ubq7v06RFKdun8+lev+BnWo++eyTdJ7kh6X9KSkJ1Kp1FNm1hXLF4KSuHsyDMPPuPuWQRBs5e5bSRopaT0zG+zudZKaV3z/I/3/9759xf8+re7C55PJZPIJM8vE9sWsRdw9CMPwy6v63KJnluj+l1r/7+9Hf3ljnbLXZqpJ9KgWW5l+2Ok5NU2t9Re76XjJE0WsfD2SxnfMGP1nzahIkqI1tSya4G5TJQ0raqHLJS1Q5Ce0zRr7srqfw8UnnQ6aWnfa183Ol3xblX6Yb6lM56srujpz5b6t1fh/8PRT6Zr2N1v3y+TbWmT+jqSD4s4EAJVSzuACVLGt5h2+YVRIjTHpP901StL6Fdw+lOlBk+4z+X251uiPrxxxw4B8kbbFLd9fzyx8uy+vadL8Fw68bp++vCZQaSsGLH1B0s7qPumyi6SNe/uykl41s4fd/U+SHkilUo+aWdjL10UPZLPZ7YIg2Nfd95O0oyp3N9gSSfdKWpRKpW4zs/crtG9Fufu6YRjuZ2Zfd/c99TG9W0vZWtLzkn4fRdGi2trae3gzp+fcvTafz+8cRdHXzGyUpJ0kVeoUfEHSY+5+dxAEdyaTyT+bVcEgM/wfd0+EYbi9mY2KomiUme0qldMb9yOek3R3EAQLE4nE3WbGsLdekMvlPifpbx/+eOvyUPtf9aieeWupzEwH/8cGmnrQ1qpN9uzXj7t/o7a29paywqUXD2pq65rq0vclFVPAfiKIgm+3zhz9ZFnXL9W4OTUNI+ovMNMkFX/wrdNlPxu6PLwktgGUH9DQsnCYRZoq80NV3Cn4D/BIstuiIDq949KxL0hVefI6mcllxnjgZ5v0eUlJuSIFNnraHtPuiTsfAFQCRexVGHnL0eebRb1+643L3nvxwOvOqNR+m946oSmhxKGBdLBLX1LftYtpM/mcyBI3vnTANZXrG1cB2y06bEjn8tSlpa53WZ2kwyoYqSdeM/mCSm/qSl704oGzX6r0vsBK7t4UhuH+7v5NM9tD0qC4M0labma/d/fbUqnU78xsSdyBSpXNZs8zs/X64FJ/q6mpubISG3V1dW0RBMHx6j4F9MlK7LkGOXe/K5FIXJVIJBbEXTB099owDL8j6Uh1v6FTTAGjVMsk3WZmP08mk3dbFd7iXA3CMPyyu0+QNF7dd6T1hdfc/deFQuGywYMHv1buZrlcbgdJ36tArp74Y01NzS/66Fq9LpvNbi1pQhAEh7n7iD66bKukWyTNqqmp+Xu5m7n7emEYnld+rDUzs1dTqdT5fXGtUuRyuR9ImvXBj7m7rv/zGzr1tudkkvb97Lq64luf0aAeFrAleSqVWt/M3i01V+Np9zera9lVch8n6/Hrbnf5A4Gyh7ZNP/CVUq9djubJ8zaJLHWV5GNKWP6Ky49rnz624q9lijbx4VTT4Hf2cdk0dd/5V4pI8ufMgtPbXm+/Q3PHFyqYsCImLJ4wqDls3s3lZ5qtoje82QONicbd06PSsfRTB4BKooi9CiNvPeoldd862tv++eKB121a7iZb3HLUZ0x+oiw4RPK6CuQqxwuSX7E8mZz95n6zY3/n/dNzjhqWTem9uHNUA4/0lZe+cd2f4s6BgWXFrffj3X3cisJ1NffxLUi6392vramp+a2ZZeMOVIxcLve8pC17+zpmdlsqlTqwnD3CMPyqpMnu/nXFN3/jRUnnp1Kpm8ysT1+4ufvwfD5/rLsfr8reCVWsZyRNT6VSN3ACVHL3hhXflyMlbRVjlFDSHElTa2pqPnJ6tKey2ezBZvabysVarWtramr6qmDeK9w9FYbhoeou/JczzK0S7jazqeW80dTV1bVlEATPVzrYx3i0pqbmP/roWkULw3COu4/74MeWLM3pazMe1FvtOe3xqXV0xSHbqmlwUYdwn6mpqdm21EwNLQuHmUdXS/bNIpZFku7Oe+q7y2bs0ad3gnZza5g8f0ez4Bp1t7sqarFM9yoMvpe5bPQ/eiNdMdZpmbdh3hNnSnaUSn1uamqV2zVRIry445L9qu4QxKT5k2qTg5I7q+A/lDRGH/98K5Rs3LQ9p93eh/EAoFcw2LEf2+qWIz838tajbjXT4zI7ogoK2JK0pWTTBucLL29x69EnbbfosCFxBwJQee6+fjabPTcMw39Kut7Mxqq6C9hS9ynYr5rZTWEYvpbNZi/o7OzcJO5QA0kYhqNyudwj7r7Y3Q9QvM8zRkq6PgzDJ8MwHN0XF3T3hmw2e3EYhq+6+7mKt4AtSdtIujoMw2ey2ey33X2tPLzg7kPDMDw9DMOX3f0CxVvAlrpvZ/+OpMdyudxd2Wx2u5jzDGgretB/NwzDZyX9l+IvYEvSnu6+KAzDv4VhuHfcYfq7VQ11vOnBN/WvTFa7bNakS76xdbEFbKmMfthNU25tssivLbqA7f6bKEx9J5YC9rg5iYYpCw+RBbeqyAK2u7rkfmkqlTsw9gL2uDmJpinzD8h7aqFkx6q056aR5Hd7pNGZ6XufWm0F7HFzxiWmLJiyS7Im+QtFvkDSPlr9862U5CelF6er4Q5JACgLRex+aKt5h2+4xa1HXR+ZPSrpAFXnifr1TT51+fLUy1vcduThcYcBUBldXV1b5XK52WEYvmJmZ6t7KGN/tK6ZnZpIJJ7P5XIz3L0v2nQMWJ2dnZ8Mw3Cuu98r6Qtx5/mQT7n7wlwud7O7r9MbF3B3y2azh+ZyuWfN7GR1D/+rJpub2S/DMPx9V1fXyLjD9JUVQ/pOXFG8/qm6hzNWmz3N7JFcLjfV3YfGHWagyWaz48MwfFrSDeqbuyyLtZ27LwjDcM7y5cs3jDtMf9TV1bWlmX3igx9r78pr9h9f02dGDNX0cdvoE421Re/r7iUVsYdNmt/gqr1epuLuaHK7sSasO6bj8j36/g7SiVenGjcceppJ15j0iTUv+AD3tyR9L9O53ulLLtq/o3cC9kzTlMVNTRvWT3O3X0r+mVL2cOlNN00alEwc2D5jzEOVzliuyQsmf2rj5g1nB0lbIPdx8h4/39ixLd82tlfDAUAfoIjdn7hsi1uPnhjlU0+bNEH94vtn65nbjSNvPfq+zX87oddvgwfQO9y9IZfLXRIEwZPqvhV7oJzmqJV0QhiGL2Wz2R+7+0D5uvrEigGeP0okEs+4ezEnzuIwPp/P/31Fq5OKyWaznw7D8A9m9osPF1Kq0KggCB7P5XJHxR2kt+Vyuc+FYfhXd79E0rpx51mDpKSTwjB8OpvNHhB3mIFg2bJlI8IwvN3MblYftGEql7uPSyaTz+RyuSnu3he98weMIAh2/fDH5jzyllIJ0+UHf1qbrVPae4qFQuH+YtcMmzS/oZAIbpRs/6IWml2SiaJj371i1NJir1mujVrm1DXWbXy1ZGlJRd5B608omdi3ffrev9TsHWIdol0/Zd4urs7FLv1AVtLg5ILc/1tR/qvt0/a+8u2po5dVPGQZJt8zef2WuyZfECTtAZeOdHljkVvUmjSJ09gA+rt+UASFJG0+b+ImI2896j6TX62+G0BUQb57kEg8NvLWow+NOwmAnnN3y+Vyh+VyuWclnaiSp7pXvaFmdk4Yho+GYVgNt5pXvc7Ozs3CMPyju6dVfSePV8ndN3T3u3K53PcrsV8ulzvCzB6U9JFb2atYnaRrc7nc9e5e7S2AiubuNdls9lxJD6n67gpYk43N7NZcLneZuw/Uf2t71YrfWUekUqmnVvTk70/qJU3L5XLze+uukQHq34rYHV15/eqhN3Xjd7fTtp8o+eaGfxQ7fLXxtPubo4TdJGl/9fwu2YJMF2ca28/QrLF9Pqdj2KT5G3VE9XfKbIK630zrqUjSLVGi7muZS0Y/ohgHCK9/8qIhDZMXnBUodZdk26ukO5T9VSk4JPPm0oPbZ+73glQ9A5FP+eMp9SfePWVKoOBvMjtF0rDSd/Od26K2L1UsHADEoJhfVojJlrce+Q3PF66RqTnuLGUaIvnPt7ztqK1f2P+6s2WqmicIAD6qs7Nz0zAMfyHpK2bV2LWoV2zj7n/M5XLTUqnU6WYW68miapXNZg8ys+skNcWdpQQpSVdms9lNamtrzyhlgxUDTS9X911R/dWEMAw3cveDzCzWW8ArZfny5RuHYXirmVXtALoeOj4Mw8+tGJj7Vtxh+gt3H5LP52+U9I24s5TDzPYKw/ChXC53UDmDP9ci/1bEXvz8+zp77BbafqOyzvwU1Upk6MmL1rPssutc2lc9f3nTKfk5mWXrzdC0MX38XMOtYfLCHfLmN1r37IRidJp0YdsbHT/R3DGFXonXE+7WdNKi7bry0XQzfbWI/+4flHXpN1EyccrSqaPfqXDCsqQfTg/OvJf5etiZO0fSNvKyXza/b263yO2V8tMBQHwoYlezdDrYcvvXznXXGarOvtelMHedOfL2o7ZqvDrxnUeOmU2BCKhC2Wx2fzO7Xur3b56VIlD3rf07rCgivRt3oGqxosfwpZImxZ2lXGZ2ei6XG1RTU3NiMeu6urpGhmF4q6SS+m1WmT3CMLzb3ffs74XsMAy/7O7/rfiHaVbKV/L5/CNhGH4zlUr9Oe4w1W7Fm663SxooQzI3k/SnbDb7vdra2l/FHaZaLVu2bISkLT74sU+PGKrNh5fSTeLf9LiVyNCTF62XDAvXudm+Rey/3ORntjXVXabpO+RLyFe6cXMSzSPuGhOZrpRso6LWur/lZidmmva+WdMt6qWEa5aeU9M4ZeFhbna+Sv83/zlZdE77tLFzq+nkdXpxOtmRb905837bjxXoqyr/zvl2yX9ViPzymaNnPlmBiAAQK4rYVWrk/Em11vX6r92LHArSX7jGta9XWC7XEZzIBqqHu6fCMLxAUosGzptnpdo9DMOHc7ncATU1NY/FHSZu7l6Xz+dvlrRf3FkqqCUMw9ZUKnVeTx6cy+W2l7RQA6dQKkk7hWF4u7uPNbOuuMOUIpfLHeXuV0gaUO1R3H2EpN+HYfj1VCp1T9x5qlUYhru7+2/VfwcNf5zBZnZTLpcbXlNTMzPuMNUomUx+pB/2FuUXsBVFUY9OYtcff886QSG83s16PjDP1WUendE2bPDlSo/q8wJ244ihR0YWXSyp2J7Kf4/Mju2YPuYvKvHYcyUMOe7ODVJtwUVuOkQqoe2SqUuunweePL91+p6v9kLEkk1eMPlT7fm2U1w2XqZyB/22y/13keuyGaNn/LUiAQGgCtATuwqNmDdxsLLLbnfzgVnAXsGl74689eifxJ0DQDd3Hx6G4X3q7n29thewV9pE0uK1vU+2uzeGYbjQ3QdSAVuS5O7n5nK5767pcWEYfkXSYg2sAvZKo8IwvDLuEKUIw/BMSddqgBWwP6DO3X8XhuGecQepRl1dXV9397s08ArYK5mk6blc7oS4g1QjM/tIEbtc7v6vQYMGvbCmx22aXjwoSOaulqvnBWwpL/MT2/617LI4CthNI+rTMl2m4grYkZndEXjy6x3Tx/xZMRawG6fM/1qqNvi9yw5VaTNanjNFh2SWr/uD1hnVU8CesnhKU8vdLWcFSVvs0pFSWQXsnMzmyu0/G1PNR1DABjDQcBK7ynzq9iPrC/lonmS7x52lT5ifMfKWo/724kHXzY07CrA2c/dPhGF4t6RPx52lCjW6+6IwDPdNpVJF9ckcCNx9/TAMF0raPu4sveiqXC73ZE1NzSOr+mQ+nx8bRdFcSeUf8ateE3K53EM1NTVXxB2kp8IwPMPd14Y3w+vc/fYwDPdPpVJ3xx2mWnR1dX09CIK5GrhvYKy0spBtNTU1M+IOU2UqXsQ2szX+nt80vXhQa2vndTI7qIitC5KfkGl68GpNT/dpK44VeWe66UjJEsWsNenK2oSd+va0PZf1Vr41mnD9oMbmDaZIdpq7N5ZSR3f3n9cUwtOXXLb/m72QsGRT7p1ykOX9fLm2kpV1wNBl+r1L5yxvWvbw7B1o2QlgYKKIXUU2vXVCUyGy+ZKvXSf+TFduOmfC/a+Mv4HhRUAMOjs7PxmG4T2SRsadpYrVu/v8MAz3XJv60644nf8/kj4Vd5ZeNkjSHHffzsz+7YX6igL2bSrt1Fd/MzWbzd5bW1v7bNxB1mRFAfv8uHP0oZWF7LGpVOq+uMPEbS0qYK9kkqblcrmwP73R1JvcvTkMw96YTbDaftjrHjdnaGtb57UyG6+e3rVm6lKkKZnmB69Rum8L2EMm375+W2vntTLbR0XdZeedJp3RNn3sDMV4+rppyoJNXbpKrtGlFa/1pklT2meM+W219L5Op9NBZrfMNipEF6mgMZJZGfc/5iU9Esl/NOM/Z9xFm04AAx3tRKrEptdPGJRUYr6ktauA3W2dZCpxVdwhgLVRV1fXVolE4n5RwO6JIe5+W2dn52ZxB+kL7j4kDMM7NPAL2CttHobhuR/8QC6X2zGKojlaOwrYklRnZte7e1U/P8zlci1rWQF7pTp3/21XV9fmcQeJUxiGe65lBeyVTNIMWst0KxQKX1YvvJZ19489iT1i4rzB2ZqhP5Xsm+p5QXiZZC2ZNzuu7esCdsMJd22ZstR/rxg62eMyqUv/ssi+0zZtTHwF7LQHDScsGOvSfZJGF7+BhXLdakFq18yMMVUzvHHyPZPXb9ul7VwV/E+SjVXp7fsKkp6Q2dGNrU27zdhzxiIK2ADWBlX9ImWt4bJkc/JarZ0F7JX23+KWo/aOOwSwNnH3DYIguFvSxnFn6UfWSyQSd7p7U9xBepO7J/L5/E2Sdoo7Sx+bEobhlySpq6trC0m/kzQk3kh9bucwDI+IO8THWVHAuzjuHDFaJwiCO7pvqV/7dHZ2bubuv9LaV8BeKenuN3d1dW0Zd5C4FQqF3Xph2/dramqeWuVnJs2vXVqX/JnJjpPU05YcS+U6KfN6+zWaO75QsZQ90Dhl4RcsKPzWZV8uaqH7ky59o23mmNtk8RR+1z950ZCGtkVnyvTfkj5Z7HqX/uWuE+uDju9kpu3xci9ELFp68YRBLYtOOCRw3WuBzlTxgzU/wP/h0mkq2Fen7THtxvT4dK5iQQGgylHErgIjbz8qLffvxJ0jboF0odJpfiaBPuDudWEY/k7dgwtRnG3y+fyN7j5Qh19aGIaz3f2AuIPEIHD3q5YvX75xEAR3aWAOcVwjd/+Ju5czWKpXdHV1benuN6vnBaSBapt8Pn+D1rIBvO5en0gk7tDAHeLYU81BENwWBEF93EHiZGZf6YU97zezj56WTi9ONpidZqZj1dN/f9yXy/3EzJsd1/Z1AbupZf7uks+VtF3PV5nLtdiD4MA4Bzg2TVmwaWcY/crkPzLToOJ38D9YVNivfcboy1+fNr6z8gmLd9I9k7bNhI2/kgU3SrZtGVtlJJ9mUWL36XtOnzpt72nvVywkAPQT9MSO2chbjvyOXGfHnaMauGm7LT/3+sEvSL+OOwswkLl7kM/nfy7pi3Fn6a/c/ethGB4jacC1QnL3vSTVxZ0jRp9NJpNPqKxTUv2bmW2Qz+ePk3RR3FlWcveGMAxvl9Qcd5Zq4O4HmFnRJxT7qxW/t34hqZwC0ECyraRr4w4RF3cfHIbhf/TC1qvsh92U6Zrk3adne9paKmemSW1NY27QjFUUxXtR4wkL/tNdcyQN6/kqc5n/tlDw45fOGPNur4VbLbfGKQtGuXSFWUltzEK5z0zVNvx4yUVf6ah4vBKcvOjkIZGFP4jcTpJp3TK2yst0m9zObfxT01PpPm5LAwDVhCJ2vNZVYNfK166TNKvj8hNFERvoVblc7idm9s24cwwAl2Sz2fv6wxC8Iq3NBeyV1toC9kpRFLW4+ywzq4qTbGEYXiVpm7hzVJnPxx2gr+Tz+RPdff+4c1SZteb7/2H5fH4n9UJLmVX1w26cvPB77n6Rev66OXL5yZlpY67v23Ycbg1TFh4i2WypqDtpIpdmtr/ecarmjo+nLcXEq1NNQxb+wN3SkhpK2OEVlx/X/ubSuzR3bJ+eel8Vd7eWe1p2Lyh/iWTbq8S7313u5vaQAjuzMdF4X3pUOl/hqADQ71DEjtdgxi98xA5b3Hr0f7x04LWPxB0EGIjCMNzT3U+LO8cAMdjMbnT3XVZ5+zHQj5nZBmEYHizphriz5PP5sVEUfSvuHIhHV1fX5u7+47hzoKrs2gt7dqRSqcf+/69ujVMWfEPy6er5a+acXGe1Nz94uWxs373KSy9ONrQtONZkF0g+uIiVyyRPtzf99VJNj+d0b+OxdzSrJrjSXQeXsDwvs9sT1tXy/qUHvFbxcCWYfM/k9U+8Z0raZEdIqi1jq38G0vlhvvDzWWNnZSuVDwD6O4rYqDpmfoykiXHnAAaaFbfjX6u1rI9qL9txxRC86+IOAvSCwxVzEXtF24BZcWZAvMzsSknFFOYwwLl7xYc6uvsDZrbypKs1TF64l9xmy3r4s2fKKfJzMs1109SX7R4mPpxqbH3nRJn9WMUVTZe4ohPbmx76ZZ/m/YCmSfM/74FdI1PxrWFMrXK7oLmxduYr6b27eiFeUSY+PDFV937dAYHbTyWNLGOrNrnflEzVnH/xqIvfqlQ+ABgoKGKj+ri++dXF6ePu45YpoKLCMJwmBjn2hgvd/TYzey/uIECF7e7u65nZO3EFCMPwJ5I2j+v6iFculztM0l5x50D1cPdkGIY7VXrfIAj+rx92/ZQFO1t3z/Ge9uAPFWlGJtJ0pUf13euXCYsHNda9c7aC4ES5F1HAtldd+ePap+8zX9qn7+8LHjcn0Thi6DcjCy41+YjiN7BnrJA/vm3mvoszlU9XtJaFLSO91c8207fU877pHxbKdY9ZcG7DAw0P0vcaAFaNIvbAUZD0pKQHJP05kJ7LFwqtNUH0/rOPb9663S4v1XXlrClfCIZZlPiCBf5Fue0paat4Y69S86utr+4s6Y9xBwEGinw+PyaKoiPizrE6ZvaGu79sZu+4e07ScjNrdvdh6n4hOUIqazBOb1knDMNzJE2OO0iVedvM/iTpL5IeiqLorZqamvcltar7boD6XC63rqRtgiDYzt3HStoxxry95SVJf1jx5/F8Pv9uXV3de2a23N2bs9nssGQyuVWhUNjJzPZW93+DarlbIsjlcntI+lUcF89ms9tJOiGOa/fQ05LulvR3Sc+kUql/qvvfrYx3F5QGZ7PZ9ZLJ5Eh331rSl9x9lBhO2SMr7h66JO4cH8fMXnf3uyU9aGbPhWH4Yl1d3VJJberugdvQ1dXVmEwmt3D3T0na0cz2cPcNYw3ez60Y6FhMz+ee+oMkDZ00f9tAfqNkG/VsmUfudll786BzlB7Vd20fJl6dahjSdaHcjpd7oucL/QX34LD2Gfv+tffCrUZ68aCG1uwpMj/F5EOKXB3JNSdVG5y05KJ93+yVfEVIP5Wuyfyr9TC5n21SOYN2X/BIP04qedvUvaYuq1hAABiAKGL3f0/LdG2Q+F/27jxKivraA/j3VlV3zwKz4IIiLnGJURKTlxiNivpGWQRcEIXE+FyCigqOM80qruUaUGAGRlBxX6IRtxhlBEFRo0ZNjBHFuECCCLgz3QOzdP2q6r4/ZjCIs1T1dNWvu/19zuEcha66l+mml9u37tXv//CEhV91dqOVQBPafm0A8A6AewFgnz+ddzjYPY+AMwD4eAMULA3aMKgitqJkBDPH2peiZUthbKu3mPkpTdNeMgzjdSLa0t0BzLyrZVmHtxf7TkH2FIPOb25uvqmoqGi97EQks4joz0R0l67rzxJRVwuWvm7/9T6AJwBc3drauo+madMBnI0sek1KQwrAo0S0IBKJvNrZjYioAW1F/TUAngFgplKpA4loEtp+Bmktg8okIjoakorYmqZdwb6KM8Ejoo2u697puu6dhYWFH3dxuxTaHgcNAD4AsBjAbGbWbds+kpnPQ9tzWE9mpuY127YnIPu+uGwE8AcAt397fvJ3OGi77xsArAXwHIAFAGBZ1k8BjEXbe+9seQ3LGUR0JHPGm4dbDcN4o3j84l10HX8AaD+PxzGD7u9Vbl/eaFaEN9Ji9CK9rKj3bGaeAJCP1wn+F7vGSY3zhnwUXHKd6z3pqR21htYFIJwM/3WIJoDN3lrZ/PU3Hi594XD1iuq9kp82zAFrIwBOd8loioA6h3jW3KFzP89ogoqiKHlKFbFzFeE1DTT1w5F3/KX7G3duzcjbXwXw6g8fH1vrkjYX4KMzlGHPaHyM7BQUJV8IIS5A9owRSQG4k5lvjcVi7/g9mIg+BfAYgMeY+SIhxBgAlwL4UYbz9CtmGMYUfH+7sRnAw47jTCssLFyX7kkKCgrWADjXsqxb0FbY3j1TCYaEATwkhJhSXFycVpdYLBZ7D8A57T+DBwF4LaYE5acygqZSqQHMPEpG7E58CeBawzBuIyIr3ZO0f7HzAoAXmHlS+1Uc5yL9S9DzEjP3EkJMlJ3HNlqYeV40Gp1BRImenCgajb4NoIqZL7Vtu5qZpwAozUya3wtBLHV8Y5cpzxpGlB4G6GfeD+MljWVbxjWaY9J+TvBrp/Ereoloy60MPs1PAZsYrxqOGP3VzcOldDCXXPzUfuQYj4JwUBqHrybicxNzhr2UJAp//Mk2zBWmkbATZ5KNGQDt1Pay7xPDBeGvYKqePXjOmyT576QoipJLsq0rLyvs+8Q5a5Ct8xcJn7GLS9acfOd9oHReNbtgmtq+B62/FsTTIf+x0bxaNJZgzCNddfF1j0F7LL6wLN3DjdaWHTVd/7BHOfi3zIpE09nQ3aW9i/tuVnPGv3/aCwFrAOwsOxUAd9u2fVWmu5WZWRdCjAdwHYCSTJ7bp5ZIJLI3EQWyiMeyrA8hv6DZkfeI6LyuOo7Twcy7CiGWARiQyfMG6EMiGheJRF7M1AmZuVQI8QSAikydMw1NkUikhIhCnc9pWdaDAE4LM2YX7o1EIhcTKEk/SQAAIABJREFUUWMQJ29tbd1f07R7AWR8zm8G3RGNRs8LK5gQYjIz3xRWvG685DjOmV113vcEM+8ohJgPYEwQ58+Qf0SjUf/L9zKMmUkI8SWAHTJ53k3N1oz9zb/sDV/3AT8ftbac9OWCMd1eRZYppRc+XY6odjuIRsH7ZzWXiOodTfxu8+wTOr1qNzAmayWJpcOI+A4wdvF3MLtEWr0OccHXNSdsCCZBr6mA4s/Ff8DMswkYmfZZgE9APLN01/I7zAFmaF9+KIqi5AvZhcqslK1FbALqBZzT1558T486QLqzz5/GnklMd0PyZcyaox/w4akL35eZwz6PX7AzkQj18i4C6j86+c4RYcZU8pcQ4jJmvk5yGuuI6JxIJLI8yCCtra37apr2KCR1jgIAM8+IxWLTgzh3NhaxiehxwzDOJqLNQZy/paXlB7quvwFgxyDOnynMvDQajf6mpx2anZy7WAixHMCvMn1ur1zX3b+goCC0L3Tbi7qrIH+kTDMzj43FYg8HHYiZDcuyriCiK5Cd789DK2Izc5EQ4t8A+oYRr6tUiOgqwzBu6GY0Uka0L7G8FUBR0LHSkBVF7FQq9WMi8n0VV1csx8WQm998dNWGxlHw+NmHGK8aMX30VzcOCa2rufeEx3fQIoULAIyG5+cIdgF6WIsZFzbMHBz+DsTK+liZjgsZdB0Av/OvW8Bc01vbct36mjFSx4dU1lfGIjH9NHZxNdK9spHQAmCRTc61dcfWrcloghlQWV+5E4DGuuF14c11VxRFSYP0WYuKJwzGzI9E44lBF7ABYM3Iu+5D26WNUjmGK60QpSj5gJnLmXmy5DReiUQivwy6gA0ABQUFqyORyJEAVgQdqzNENJY57dmIOYWZrzUM49SgCtgAUFhY+B9mviCo82fIzdFodEQQBWwAIKIm27ZPBRB+B91/c0jn8u+0aZp2EeQXsL8AUBFGARsAiMiOxWJXMfNv0TZ26XtLCPFryC9gp5j59Egkcm0YBWwAiEaj96Ptqgs1G7cTRJTRUSIuA3e8vN5dtaHxRHj/XLzSAcaGWcAuveTpci1aeAt8FbBhg7GQ0DpeRgG7T2V9SYmuzWDQTPgtYDN/BqKzk3OHXS67gD1p8YQ9I1H9VnZxK9ItYDO/D6bT1m/aMC7bCtjVT1SXxZfHxxmGvkKP6IfJzkdRFKU7aiZ29mMiOuejk++4O8ygq0fdNWe/J845iIGzwoy7LY05qzoOFSXX2LY9FkDa43R6ioieNAzj1+0LzsKKuZmZjxdCrABwSFhxt7GzZVkjASySEDtM82Ox2JVhBIrFYo9ZlvU8gGzclfCH9jETgc6zLCoq2mBZ1mQA9wQZpzOapoU2c56Zo0KI34QVrxNfMfPRsVgs9KvBYrHYH4UQXzDzYgAFYcfPEmdKji80TRtlGEZ92IGj0egbra2tR+i6/iIz7xZ2/ByQ0SL2n97+HL9fuoYBeP3y+d9wnbGb5x3/QSbz6EqfyvoSu5XuJuBEeC9gOwy6ubG8YDrM4eEtnGxXVL1kV5ewkNgd7m/xJADg76Rr5ybmDF0JSJwVzaDqZ6tPcjXMBGM/pHWFDLcCuEXnyIxZQ2Z9kekUe2L0otH67uX9RjLzdHb5Z0Skg3Ek2nY2KIqiZC3ViZ3tCNM/GhluAXurqMBEgCW+4HKuLfRSlKzBzMTM50pMYVnYBeytiKg5EomcCEndbER0voy4YSGiP0cikVAXWBKR7JE4HXklEomcE9ZCpkgk8gCAf4cRa3vM7HOOafosyxoOueNjtgAYLqOAvVUkEnnedd0xAISsHGRpaWnZE8BRElNgZh4ro4C9VUFBwRrXdYdA4tUX2SqTndh/+ziJS5/8AK226/Wqj01EPDY57/g3M5VDd/qNe6rI1elBAvkpYINBdY3NO06FWRF6Abv3xMU/jAD1zHy8zwI2A3jIMbQRiTnHvS2zgB1/NV4YXxavJQ1/BPBDpFfAfkvTMHR9w8Yps4ZmVwF70vMX/6J/eb+lgPYgiH5BRDoAEOEX5gpTNTkqipLV1JNUNiOev3rkXTNlhV815s5N+z4x9lIAd8jJgPrLiasouc+27SMBhNY9uZ0PIpHIKTIK2FsR0ee2bY91XXexhPAVLS0tPygsLPyPhNiBYubPIpHIWWFdXr+VYRgvCCE+ALB/mHG7kAj7MU5ETiqVeoCIQumA3y52aKMdNE07k1li8x3z72Kx2N+kJdCuoKDgKcuyqgAskJ1LmAzDOIOZpTXZtO81eEBW/K1isdh7QoiRzPwC1Oc1AG07Epg5I58N1nzVjHF/eAdfN3n+nqiJCOcmaoZnbHFvd8qqnyhrgv4ggGFt9V0vSBD4kmTtcTXwflBmMFNZfMnRrosHAd7V59HNDJ7RuGHLDXhkTKjvL7ZlmqbWMLDhF9zEdxAh3TFam5lRa3Bk5qzBs5oymmAPMDNd/PzFexuufpXr4NcAovzdh8hPkqlkCYBN4WeoKIrijerEzlIMfIBo8STZecTE5vsZ+FRGbAapTmxFSRMzh7KAqwMtzDwmyDnJXhmGUU9ET0oITYZhnCohbhgmBjX7uStExMz8aNhxu3AFEYXe6U9ET4QdEwCYOZQiNjPvwMwyFxvfEovFsuZxFo1GbwEQykzubMHMZ0gM/0o0Gg39S6LORCKRV4joMtl5ZAtd1zPShd3QLHDhg6uwPuH1O0gSIG1iomZYaM+/JfElfcCx+wHtOM8HEVoJfEliw+a5CLuAba4wSuLP/JYZjxPgt4D9Bdg9s7F2+LUyC9iTl04uTh7RUKUxPUtIq4DNAL1mwxlSO6T2yllDs6eAPXnpBTtXL6u+wnD1NwCcgc7G5zD2cqPuTqEmpyiK4pMqYmcn1onOW50F24FXjXnE0kC3yInO/eTEVZTcxszlAE6REZuIbojFYitlxO6I67rTEfaHOQDMLOXnH7DlsVjsIVnBdV3/q6zY23k7EolIeV2MRCIrAYT+JQJCWrInhDge3mfTZtraSCQivXlge5FIZBwRrZedRxhSqdRP0HbpvpTwruueSUS2pPgdMgzjJkhcVpxlelzETtkupjz+Ad5a3+jtAAYT8cxkzdCFPY3tVZ/K+hK4bg0TjQDY4xgLbiHm6YmygnmhF4LNFQWlidYpGuh2EMp9Hr0SrjM8OXfEY4Hk5lF8RXxfV7MfAOgmpLdLJgHmayPNkSF1g+tey3R+6Yq/Gi+sXl59uqPFXtSIrgbQp8sDCBq59JNwslMURUmPKmJnp7s/HHnHX2QnsRXr9Lik0KXgdGaQKcr3mxDiRACFYcclovWGYcwJO25XYrHYv4joaQmhD2lubs6rq0mIaIbM+LquvwYJX0hsz3XdK8Iep7IVEbnMLOMDcljjRAaFFOc7mPkSImqRFb8zRNTY/mVc3iMimff/3IKCAikz57vSfhVKFQBpHapZpEdFbJcZNy37D/680s9FNHxPL2y+oSdx/dhx6pO9bZ3mEtHp8DiHmYAWIm16ovmT+TArQv0SZsepT/YuSbbOBHA1+3nfyWAGPUm6e2KYM8Y7El8WPwkOL2NgJACv89G3YgCvg2lk6avlV9940o3Sr0IE2kaHTFpWNZCa+Ali3AOQ9/GCjJ8Fl5miKErPqSJ29mGXNWlzsDuy+oTb3wPwtYTQxkHPnlEkIa6i5LphMoIy8zVE1Cwjdlccx7k95JCNAF6NRCJ7hRw3SP8yDON5mQkQ0ddEtFFmDgDWxWIxaQvf2r0jIWYpM0eCDNC+jPbYIGN04fVoNLpIUuxuRaPRPwDImu6+oDCzrCL219FoNLRCpV+xWOwdAGG/jmWV9pFGPerSf+CNjbjt5U/8fBO61LWjU9bXjAnly63+8UWFViq6gMBnwnsx1QLjkkRpbD4Wnh/qItiy6hVlworcRcwXAfDz+uAQYYGG2NmJ2SM+Diq/7kxeOrk4vrzqeob7IBh7pXGKFDFuskiMqBlS86Jpmm6mc0zHRc9e1G/i8up5LuhpBobC50x9jTBg9KLRfov5iqIooVFF7Oyz7N+jbv9QdhLfQmAwXpEROtUaKZURV1FyFTMbAIZICL0pEon8QULcbsVisSUI5os4F8BHRPQoEV3JzCMdx9k7EomURaPRgZFIJGuuqMmABUQkvQuamaWOVSCiO2R1YW+TwypJoQuCPLllWQOIyO8s1Yxg5tnZ8PjuDBGx67pZW2TNBGaOEtFRkmLfTkRJGbG9chxnBoCsGnUSJsuyBsJjZ3JHXvtPAtc9swatwuPTN+Et3eFzN88fFE4Tj2lqjdx7JhFOB8jr53Mb4OpE+es3h92BXTx+8S6M1nqATvWRLwhoYcbERFlBdaK2QsZoLABA1TNV+7u6Xc+M6QTy3zBF+ICB40oGl02fP2i+jEav7zBXmEZ86cXjImS8AdAEAGl9hmbG/gMGDFBFbEVRspbadp1lmPhW2Tl0hAh/Y+DEsOO6jlYKQHbnnZK9qPepB97EwMld3kgnkK6DdA0U0UFG2y9ENGiGDs0gkGGAjLY/13Qd+Oa2bbchQwdFNMDQoRla2//rHt63M6Z8NOqO0Eby2Lb9K8D3TMJMuDsbu7ABgIiEEOJFZh7Vg9M0AFiJti7YlWibi7yKiLJmcU+QhBCyxkp9CxGtZ+ZDJYVnIcRdkmJ/g4g+YZZSb40BCOxSaYmjJNZFo1EpCzP9iMVii4UQHwHYT3YuQWh/7eolI7TjOPMlxPWlsLDwYyHEE8w8WnYuMhBR2qNEPvqiGRP+uAoNzZ4blT/RQWdsqhsWzpem5gqjJNk6m4BKH0elwKhKzh1+W2B5daJX/M8DdNYeAuBvdjLjayI6q3HucYuDyax75grTSFiJkaRhPjN2Jr/fizAsEP8h0hSrypbRIaMXjdZ367PbIUm7YQ407Vc9PF0TwC/jC2RFV7miKEpHVBE7u6QoWiz7MuUOMSDnci/NKZESV8kJxace8HsGul7ERQRoWlsBu73wTIYGMqjtv3WtrWC99ZfW/v/Gf38Pxrf//5vf94DAvTPxd/XKdd1hRFJGyd8vI6hXzPwyAC9FbBvARwBWEtHbRPSOEGJlYWHhumAzzGrvFxcXZ8WXiZI7sd8rKiraIDE+AMB13c9k/Btvbm4OdOEiEVXIKM4T0V3ZtsyvI0TkWpY1H0Ct7FwCUiEjKBHVFxUV5crizDoA38siNoC0uvQ3NQlc8uQHWNfQ6vkQZpy3qfa4cK54Gff3SGnDF1NANN7HUc0AT06WvxH2iBnqPXHpYZrr3AP/X6athuae1lAz4u8B5OVJ9YrqsqTdcClpdDHavpT1h7FWAy5d17Bx0SNjHsmGGfVUtaxqdw2oBuMCgHqyCycF8DJmmlEzuPbVbL4ySVEURRWxswgDb6wZXpeSnUeH2F3n42qxjCHogX5oVnJX8akHzCDQtO5u951CdAfF6O8Usrf+2ub22/95tiKiwRLCfhyNRt+WENePjkZ7fA3gn/hvd/XK9u5qz592vyekzsLeFhFtlNSFDAArZAXeVjQa/UyIUEefAgA0TfP/od8HZpayTMp13ay4ysCLSCSySAgxB/k5DlDK/c/MOXP/G4bxsm3b65m5v+xcwsTMpUKIg/wel7JdXL9kDV76aJPXQ2wQpjbWDlvqN1ZaRi/SSwu/rAbRlfD+mbwJRJOT6zffjtoQZzCbplbScOhgOO5dIOrn72B6iQnnNNaMWB1Mct2LL4nvC9u9FSDfexeY2QHREk3TJs4eNCcrRn5W1lfGdEMfQ4TLwNg/3fMwmAl4nYBZQrhP1w2vS9Xm7fekiqLkC1XEziIEvCw7h87ohr7OccIvHDjkqseo8h3Fpxz4ewK8FbC7KER3WMjuotiNrb/kdDp3i5kj6XzQy4CnJMT0JRKJ/FMIcS8RvQdgpWVZK7OluzjbMXNWFG8BgJkbJcbOlp9DA4AU0ukk64Egi9jtRardgzp/Fz5qX5qXE4joU8uyXgUwUHYumcbM/kYTZIaIRCJZ//q1FRGxZVmPA7hYdi5hchzncHhfdPiNe1/bgAf/5udlnuck5wy7CzV+I6Vh9CK9tF/xuSBcCc/P5WyBtBuSpbE7UDMmvE5g09RKEof+mgg3A+jj40gG+DHbciubFoz4LKj0umKappY8LDkCGtcBtGcap0gS4fpUg3XLgjELtmQ8wTTEl8QHQOeZAIaAfS3U3N4GgGbyFtxfc3KttPnkiqIofqkCYRZxibK2iN3rU219cmfHRcjdP+T26MVZyUPFpxz4eyJc0t3tvilge+m43qZ43V2hm7TsLGADgGVZBxBRqIUtIKuKe51qHxdwtuw8chEzZ02XPTNvljQuB9Fo9CUpgbfTXsj6HMAeIYcO7Moo27Z/jB4sbeuBZyTE7BEieoKZ86qIzczFQogfSAj9OhF5btPNBu33//etiH2k3+f9V9Y04Nr61bBdzw04jxc3O1cnwxmjQGW79T6JgVnwPAeeBIOua2za8SbUHBzq+KPShkN/B6AW5GdmPbtgujUqCqclF1RIKf6aq8xocmPDVBBNBcP/aD+i9xh8btnL5a+bZohd750Y99S4ouKCwgkAXwJ/XyZsh1vBWEgxfVbN0XM+yViCiqIoIVFF7Cxi6FbWFAq29+b5C8W+T5zzNYCdwoxLLqkitvKN4lMPvIHQfQEbGnXfcd3ByJBuC91ZPEYEAIjof2TEdRzndRlxlVDYsVhsrewktiGrE+pLIvpKUuyOhF7EJqLAitiSunC3zsrPKcz8ouwcMk0IMQASRqTk4v1vGMZrQojQr8SQye9Sx483teKCh95Fq+2x7kh402lpuXDjwlGhLKcura6vYOBeeF9k6jC71zWWv3F92CNEyhKHTGCgBn464QkWGNckN26egUeGSZkdPX7x+F2SGxPzARoJ9v3c4oD4oVis4OIZR85oCCRBH0zT1BqP+PoQZn0ugIOR/nOlzYwVmutOKXlth3eyoTCvKIqSDlXEzh6pD9/c+1PZSXSjESEXsZlIPUYl2ueJc67VwDvLzgMAGl5ZfUjr2q+6n5lJ6KAQrX+n6xqG3kGntt5BIfu/yyBzwM8lxPwkG5bdKYFZS0ThD2DuhKZpWyTNxP5IRtAuhFJs2U6QT4JSitiO47wqI25PRCKRt4UQm4E0Oguzl5T7X9f1nCtiE1GrZVlvAjhcdi5hYOaYEOJgr7dPtgjEH/0XPmu0vB6yVoP7u+Sto75IL0N/SuPPHgzXedRHV7PDoLrGjZuvx9wQi46V9bGyBlzKRJfC3yiXJmJclKgdfk9AmXWramnVoRrRnQAG+D+aP4OmXVqqld1vHmlKX/hb+VLlTkmrYRpYmwBCQZqnYTDeI42vLTXKHzMr5P+9FEVRekIVCLPHJ8j+b0RDn0VKaia2VAT8hkH7ys5j89vr0LrWWxNkx4Xojruuu52Rvc187BwR+mIsIvpn2DGV8DCztEVMHWHmzZJCZ1URm5lTssaqBORHEmLm5BdwRGRblvUGAN8LyrIVM/9IxuNZ1/U3Qg+aAcz8ChF9L4rYtm0fCngr3lm2ixuX/QevrPE4IYaQJMetbpg3IpS5+H3iSwY4rrMIhHJPBzCYCAuSZbEpqA1xBnZ8UWEp0zUMVMNPrYDxNbF7TmLeiD8Hl1znzBWmkRCJ0USoA7CDn2PbFhzSa47LlfMG17wZUIqejV40Wu9X3m8QpehGAD3ZdfM1A7cWFKZmzzjyFuld5YqiKJmgCoRZgkFrZefQLcbmsCdWcjYPIFZCsfntdWh611udodtljd2NFumg2J3Nixw7cGDYAbOtyKlkFhFl1f3LzE0yil3Z9nMA4LnNMEfsJiHm+xJiZspK5FERm4hk3P9fE9GXEuJmwkrZCYTI0ygRZuCRf3yG+17bAI9jsAVcXJ/oMzyUxZ5lExbv6TDfA4K32e9tVdX7dIcvh1kRWufsTuMX9Uq5va8B4SL4qxOsJXbGJuaOeAHzEPrlUpOXTi5OiMRlRKgGUOjzcBtEd2q6fnlNRa30sWFVy6v6aowrATobQFE65yCQ5YLroeHa2mNq3wKFf58oiqIEJWfaC/MdMa+VnUO3iEPvxFa+3zb/8xPvBezt52Bv20XdXcd1F/Oxc+V7FGYuhM/OkwxZIyGmEhIi2ig7h20xs5TLYF3XzaoitqZpKdk5ZNiuEmJ+KCFmpnwgO4EMU/e/D0SUb/d/p1zX9VTE/uf6Rly/ZI2nOdhEAIMfSpbvMRcmBX4VbO9JK3bkCN0F0C98HPYnDa3Vm+qGh/bZa6fxK3qJaO9biVAJwPNOIgZWMWNMYt7xKxDOYsxvqV5SvaujOfcSYSr8FrAJCQbO37Bpw8WzK2ZLLWAzM1UvrR6mMS0D6EKkWcAG8BETzqIG+m3tsbX/UAVsRVHyjerEzhJEtE52Dt0jVcRWQrP5n5+gadV6bzcm6roQ3dXokK2F7s46tXNEKpXaTdPCr7gTkSpi5zFm9nhddmikFLE1TftcRtzOMHPedGIzc7EQokRC6JwuYkqaDR8UGUXsnC0ERyKRD4TImlUFgWFmXQjR7diUZIvApMfex5dbvD0t7tQruv4L0VINc0Dgz6N9Jy8tbrVTN4OpAsTe3qMxnnONgnGbZw9LBJzef1XWxyyt9S4wTgX5uu52JVz91MZ5Q6SM3Kp6puogMnAfmA8CfF4vTFgFl86uHVzzpuxCb/WK6rJJz8WvIw2/Q/rFawHwzbobmTFr6KxQZrwriqLIoIrYWcIFZ/2cKmZszp2pCkou2/K2jwI2sM2yRo8d150Uu7cvdOcSXdd3k1HUYOZsX0ir9EC2FbGZOby5oN+Om1U/BwB504mdSqV21bTwn2+Z2fuLTJYRQqw2jLx6C98v7IC5fP8TUaNlWV8AyIrF20ERQvwM3SwwtV3GpU9+iHc2eluXsGOvKM49fI8J04btF/znrsr6WEo4M0D0a8/lVcZrguiM5tkVoXUF9xv3VFGTTvcDGOXvSH7JFdFRm+cP+jqQxLpgmqbWeFTD8a6Du8A+518zOwQ8YpF90fwh80PPfVsmm1ri2cT/wsZCBvZJ8zQOgDfgONU1x9Xl5Jx/RVEUP3KrSpPHiLO/iE2E/G/7UKTb8vYn2PKujwJ2ussav/X7310GmUNzsAEAruvuLiOuECJXZ4oqHmiallXFW1njRGzblvpBtwN504mt63roBUwg+7rr/SgsLPwcyI9LxJm5F7opVAaBiHK9U/Ez2QmE4KjubvDg3zZi0T+8/SgKDA01pxzQNPW4fRf3OLPumKyVaqhmwnjvB9G/4PL/NdceF1pzQPm4ZaVNhcY9AE72cRiDeRmTdrKUAvYqM5ocmKyEoz1MIL9j9JIacFnpbuVnzR8kt4BdvaK6LLEscR1pWELpF7A/BXh6U2vzIFXAVhTl+yKv2jhyGUFPys6he2TnyWcmJUttWemzgN1NIfqb3zO27bzWuy9058gc7G1pmtZPRid2UVFRVhU5lczKtg7kwsJCR8Zl9IWFhVn1c4CksSpBcF23r4xlnY7j5OwXcEQkLMtqANBHdi49lUql+krqxM/Z+79druffLSIa2NX7mjfXNeKaem/rCnSNcHHFnjj2R31eIKLAr+gpSS4ZA6Jr4LlhjDdo5P66oW5EaCPaSuJL+riufSsIp8DrKA4GA3hYd3H+prnHhT5mctqyaaXJjcnrAB7P/pvxPiZ2x5e82meJaZqBz0LvjGma2uYjGg53BWaD8Ev4HYPSxiJCPTNdWTO49p1M56goipLNVBE7S5DGWV/EJmbb4zQ3RfFty8pPsOUdH1f3br/IsaPRIZ3Mx+60Y9vIvTEiWzHzLhLCbiGiVglxlZA4jqOKt0ArETVLiPu9QETFMuLGYjGpS7wy4EvkQRFb1v2vaVqu3/+53kneJWYmIUSnSx2/2mJhyuPvI9ni7SVhyAE74sKj9kDU0F/KWJKdKKtaehTDXQAg6vGQr4n13zbUDg2tGFkSX9SH2L0NRKPgvdDuEuEhTrmVm245PvQC9sSlE3e3kLoFjGEg3wXsV1ybz5k7bJ7UWfjjV4zvlbQbLgJoCii9529mXk+adjlvwqKaMTUtmc5RURQl26kidpawYYe3vCNNrLENVcVWAuC7gE3kbVljdx3XHczHzmHpLoLpCfXmOc8VFBRskZ3DdkKfiU1E2VbIzzcFkuLm+vNXXjwuiUjd/+nJ+jGEPWFZ1v5EtFNHfyYcF9cvWYNVn3p7edpv52Jce8J+6BUzwMyBFrF7VdYfyOTeC0a5tyNoC9idkJg75C9B5rWtPpX1JQ7T7QBGwns3s8NM86Oi4LIvb6kI/X1BfFn8Jwz3HgD/43PxpAvmuy3NnjZ/mNzxIfEV8X1hu3UADUI6NRiGCw2PE2nTagbV/DvzGSqKouQGVcTOFno0+zuxQY4aJqJkmt8Z2AA676j2sKxx+2L3tssgc1xh2AGJKG/m8iqdyrb7OPSXIWZWXdjBklXEzLbHtl+5nj+AtiK2pKXEub4cNdfz7xIRdToP++E3P8XDb34K18PjpneBgRtH/hB79ikEgOZIJPKPDKb5LcVVy/vqJO4HsJfHQ1JMPK2xZtgizKVQ/hHsNH5Rr5RODxBwPLyPsXAYXNdYvmUazGGhP+/En608msEPkv8FsE0EXF6yW/kCc4Ap7fnSNE2tcWDDaLZ5HkBpLWNl5vUa6dVbyrb8eeHBC9WOKkVRvtdUETtL2KR7W6stEbOaia1kVk8L2N8qRHcyNqTDQnYHyyBzbZFjB0IvBDFzXhRRlC6pD0vqZxAoSUVMO4y5uEFi5pSMWeIBkPIlRq6/fuXR/d+ZDkeJrP6yGb9f+m8ID201hkaYMugHGLjvN1MbXg3qy/fyactKnZS4DcDPPR5iE/jGZOkbt4KGhfIE2Hfy0uJW4T5CwHE+DnPAXNu4ccs01I4J9TnTZFNLPJs4DcR3kP/niXWs4YKaY2ufCSQ5jyYvnbxzo564npnGwv8MbwBIEfC4bbv36Q32AAAgAElEQVRTa4fP9feBSVEUJU+pInaWKItssdbJTqIbBKhObCVj/C5xBADadg52B4XoLkeHGN895ptfObjIcXuSCkE5XQRQPMmbBYI9oIrYwYpJiJnzj2tN01pldDBnmuM4MRmLHZHj/67z5f7vQodF7LoXPsYXm7299Rjx451w7hH9v/l/IgpmZIe5KOokHJMYJ3rsbXZBdHfC5usR0oLBvpOXFrfY7s1ENNRzQxLDYbjzGjc2TcMjIRewV5nR5PKGKtLoBoD81iv+Dp3Orj2mZlUgyXlgmqbWcETD4Q7sOjB+luZp/g3QFSVG6SJzsJnzr1mKoiiZoorYWaLZKcv6FydmiLT2JyvKdnzPwAa+mYP93UK03kEhW++g2K13UOjW82GMCADAdd0CCV1Z6hkhvzERZf1rUwhyutiV7SQ9d+XD+9+c7iTfSuJM7Fx/DOTF/d+RlpaWPQDs2dGftQpvf+0f7FCI6078ISLffo+X+XnYpqmVJHqdT+CLPM9qZizRYvoU1BwXykiYncav6NVqp+YS+Ex4f99mg7i2sazpMswNuYC9Ynyvxo2JawG6CP7+nboAntJdY9yswbOkLT41V5gFSdEwQQNNB7CD7xMQBBhPsY1JtcNq1mY8QUVRlByX62/g8sbqLZ+pQoHyvdC0amMaBWy0jw3Ru+y27mxZY1ejRfIFEYU+ExtAVEJMJTyqeNtG/RyCpUuIaTCzRkShdEEGwXXdWJ6Mk5Bx/4OIcvr1K4/u/+/QNK3DLmyvCiMaak49ALuUfOsij5RhGK/3LLPvKv36kEHQ6ffw/pl6pQ1rbNPMYeHsQjr77gIr1jIHTGcB5PVNrw3GrGSy8GrUDg/1irvK+sqdErZeR8Cp8PfcIBiYG22OXnPjSTdKG9EZfy6+W9JOzgXRSUhveeOncPmKhmjyD/cMvqc18xkqiqLkPlXEzg6MMY/kbUeFomxlfZ6E9XkCsV1LfR1HOgHa1s5p6rwobRCg6yBNg6bTt/7s28XrtnOA8BWAfwbzt23DoI1Bnv9boUJGRJGwYyqhUsXbNurnECxZC+oiEmP3mMQO5kyTMpYq14vYkLcQNXBE1KMi9sRjf4DD9i7f/rf/RkQtPTnv9koufmo/JrqbgGKPh2zQiX6brD3p80zm0SlzUbQ0UXI1mH8H7wXhtgK2yybuqQj1+TH+XHw3uLiX4R7jZ1ENMzeThillm8rvMMfIW+AYXx4/Ei7fCWC/NA5nMJbBoIlzKmreIwpn0aeiKEouUkXs7KC6sJXvhWjfUkT7+itgB4UBMPHra0beebzsXDKBmVvC7spiZlXEzm/qtamNKmIHSNO0lKTZvjHkcBEbcmaJZ5yu662uK6UhPqd/fkSU0/l3I+0i9tADd8K4gbtj+1UnzJzRedi9Jzy+A2nGgwD6eTykEez+blPtiHDmNC9apJe92utyBk+G54WCJMDuTcnywqtgVoT6+l/1fNX+sPlhEH7qdSpLu09J08bOOXbOUlmFX3OVGU1ubJjArnsdERWlcYomJlxTZpTNMyvM1hrUZDxHv8wVpoEX4ZohzWxXFEXxQxWxs4P6gKwoSo9omtYioRCUzx+iFWUrdaVUsKQUkltbW8sANMqInSG9ZCeQCcws5f53XbdMRtwM6i07gSAw845CiAPSOXavHQpx/Yn7oSj63aZjTdMyNg+7f3xR4Wa34GaAfuHpIjhGKxHHE3OHL89UDl0avUgvfaXXZCZcBh8FbCL8PlE27GqY4Y5Zql5S/XNy8AQIe/g5joF3NThnzhlU95aswm/8ufhuyU+TNWA6hcjrz/obDOBtjXjc7EFz/xZEfn4xM01cPvHHjXbiGu0wYyKA/8jOSVEUZXv5MxA2t6luN0VReoSZZczOK2FmKfNMFUXJD7KKmLqu7yQjbgbtKjuBDJFy/xNRTt//zLyL7ByCYFnWQKSxNLooquOq4ftizz4drgdxDMP4a4+TAwBzhdHIJZNBdCrAXvK0CZiVKCu8DwihU5iZSvv1GgvCDZ5nYDOY2JmZKB0abgGbQdVLq48hHfWAvwI2gBe0qDZ8zuC6t4JIrTvMTPFn40ezy/VgHg2/BWxCCxMWwqFjs6WAPWH5hB0mLp94GcAvMDDMZlvN5FYUJSupInZ2UF1eiqL0lIw3mwQg17vZFEWRq1lG0FwuYrZ/ebiD7DwygZkzOqfYq1y+/wGAiHaWnUMQ0pmHrRHht7/sh+MGdHqX/pOIMrFIkUqTqVEEngpvVzMzGI/GItqMsMZzlMSX/h+IanwscXQBvi3R0veasAvY8efivyYNDwLo6+NIF8x/LCBx6pyj53wSVHpdMdnUJj1XPQ7EjxFwUBqnWMfAeWV62UU1x9VsyniCPo1eNFqPL48fF3Uj9Qz3GgB9AObDhx7+hezcFEVROqLGiWQHNW9KUZSeklIISKVSfQB8LSO2oii5j5m/DHuef3vcXC5i7gTvi9qymuM4X+p6+H+VHL//QUS7SJolH7Qui9j9yr67z/Kn/Xtj2pC9YWw/CPu/MjJKpDy++Mcu8yx4HeXDeN2JaFWfzxralIn43Smrrh/J4DnwvmjSJeYFsYh+SXLuwaGNthy9aLTe/9ld/4901DDwnQ2cXRAA35yKiCtrKhZsCSzBLlTWV5Y0Ppe4gZnGoW05sGcMZgIt0wy9anbF7A8gYSH79qqXVO9KOkwwnw5C8X/nkVNyDI1RTXaKomQl1YmtKIqSB5hZyht6TdPyohtQURQ5iOhzSXH3lhE3E2zb3kd2DplSUFDwOSQUc3L5/mfm4nwcJ8LMvQD8T1e36dsr+q3/L4houPHk/VFW2HlfFjP3uIjdp7K+xHW1uwHs7vGQL1zbPWvLrKGhdLOWVz0zEKAFAHb0egwD9xW1ONPCKrIDbV3M/cv7XQhdu4WZ/RSwU0S4qrSh/JIFkgrY8eXxvY2IXs+MC+GzgA1AEOP3umuMml0x+31kQQF74rNVo8nAqyCch+2++GDmLyWlpSiK0i3Via0oipIHiGijjLjM3E9GXEVR8oPjOJ9J6sTdN/SgGcLMA2TnkClElLIsKwF/HZk9lsv3f/viw7xrRLJt+3B089l0j21mXhOA6UP2xs/6l3R1CEej0Vd6lNjoRbqj060AfuHxiBaw+3+b54/4sEdxPSqveuYgF3gIXufkMxiEP8asgsqNCytCG+c07rZxkcRziTiBbgD7upKkkTRUzTm29p6gcuuKaZpawxENg8F8N9LbRfAxiM6tGVwbzmLPrjBo0vOT9nBdezaDRnZxP3waal6Koig+qCK2oihKHmDm9TIuydc07QehB1UUJW8UFBR8JkRoV7Jv64cygmZI3hSx232GkIvYAPZhZp2IcvGS+Xy7/wEAruse2d37mG2L2P/7wx0w9vD+3Z12FRH1oKuUqaRf/QQAp3k8wGbmqY1zh4dSsCyNP7234+IhInT7g2jHAC2NWrFxXy6oCK2j2VxkRpPlDVeDcYnPQ78k4rFzjp37dCCJdaOyvjKWjCYv0Jiuh/cxLVs5IDwL0EU1g2r+HUR+fpgrzILG5Q1jXDg3ALRb17emj8PJSlEUxb+8+xZfURTl+4iI1suIy8yqiK0oStqIqBVAQkLoHzFzrr4P/qnsBDLsMwkxY6lUKlfHsuTb/Q8AIKKB3d1mx95RFEZ07Nw7imtP2A8FkW4bev/Sk5xKq5+pIE27xuPNGcBdjS5uByjwcRG9Kut3AusPEOFAzwcRnncNcUaoBewVZxckyxuuA9NUn4duYBsnzRkkp4B94dMXlhuGNp9ddzb8F7C3EHBtSrfGZEMBe8qzF++XdBL3M+hOAN0UsAFNw7oQ0lIURUmL6sRWFEXJA7ZtrzeM8J/SmTln54oqipI1PgLwy5BjlliWNQDAOyHH7RFmLhBCHCo7jwz7CEBF2EE1TfsVgFBGPmTY0bITyDRmjnp5XMd0Qt+SKC48cg/8sG/3dcWezMMum7B4Txc0jxilng4gesFpab4Ct45KpRvTqx2nPtnbsnAngMN8HPaPiNDP/OrmYV8Fldf2Ji+dXNwo7GtAuBjko3mOsYoM7fSawXPeDjC9Tk15rnIf29VuB6gijWsc15HOlZ98tXHxI2MekXqlR1sHfPJ0Afc6Yngf/8esOrEVRclaqoitKIqSBwoLCz8VQjiArzmDPaZp2k/CjKcoSl56H+EXsUFEv0KOFbFt2/4VgALZeWTY+5LiHgbgPkmx08LMpUKIvOvEtm37lwAKu7tdQUTDbw/uh9/+cld4KS7atp1WEbvv5KXFrbZTS/Dc5fyxo7kXbbl1VPCLHCvrY8KiWQQc7+Oo1SBn9Fc3Dwttf8r4FeN72baYT6Dfwl/N4e9MOLPmmDn/Ciq3rkxaVjXQdukuAPv5PZaBl3XXPm/24JtlPad9o3pF9V4JO1FDwAgC+VpE6bhYG1BaiqIoPaaK2IqiKHmAiBzLsjYC2D3MuMzcn5l3JqLgP7hlgGVZP2PmvZj53Vgs9m8icmXnpCjfd0T0L+bAr77vyBEAbpcROF2u6x4jY/9BkIjoPYn3f06xLKuCiMLfhBq8bkeJAECBoaGyYk8Ymqd/A2uKi4v9F21NU2tJ8EQCnQh4qZXTFoY9Ycvs49/zHcuv0Yv0Mo0uZ2Cst9wAgNZpLp3cMG9YaGMtJi+dXOza9kIGfg0f40uJ8JIbxW9qj6oNfbGgyaaWeD5xuss0F8x+Z/S7AC2wjNT0BRULQhvV0hFzhWk0iobfsI0Z8DA6pEM6PshsVoqiKJmjitiKoih5gplXElGoRWwAcBznFwCeCTtumi4ionOICEIIy7Ks1US0ynXd9wCsAvBeNBr9lypuK0p4XNd9X1JhdliuLfcjojGyc8g027bf13UpddmftLS0/KCwsPA/MoKng4h+LTuHILiue5SX5wAiguH9qeLFdHIpTR4yCOCp8FZ8dQC+obH2+MXpxPLFNLXSZO9zXBeTyeNneAI+1Vw6bdO8oe8Gnd5W41eM7+UI+24Ap3o9hsGsgZ7UnNbz5xx1a+hNEeOeGleUWJ6YQsClAEd9Hv41My7ZkFh/t+zxIfHn4rs12omZTHQa0tl9RmAw/8u1XBl7KhRFUTxRRWxFUTrFjJNBFJOdR2CIk7JTyLC3AIwIOygzH4LcKWIftc1/RwEcyMwHbvvhWQjRaFnWewDeBbCKiN41DGMVEYXeGaQo3wfMvEpSEXtn27YPA/CyjOB+WZb1cwD7y84j0woKCtYJITYD6B12bF3XjwdQF3bcdDBzsRDiBNl5ZBoza0KIwwM4te+ljmXVz+zFjDoAvTwe8kSyed0sv3H8YypJLB0GYCYRexsnRGhwHPfCxLxhrwab239NfXJqbyGsu0DeC9gAQIxHjaLoOTcOrN0cVG6diS+J94HOtQBOh//C7zvMuLB2SO0rAaTmmbnCNBJ2Yii77lyA0lpYy0AzMf2BXO3auuFzA5/rriiKki5VxFYUpVNrRt0ZWueGkhFvyQjKzEMAXC0jth/MvKsQwsuMwxIAv2r/BWaGEAKWZW1Ce2EbwDtE9J5hGO8S0dfBZa0o+S8Wi30khPgKwI5hx2bmk5EjRWwAZ8pOIAhExJZlvQZgsITwo5AjRWwhxCkAut9mmGOEEAcBKMv0eV3X9VfENldFuWHdHBB+6PGIDxxDm4CF5wv/2flTWr30fwC+DV5/ToxWMKZsnjv8z5gXbG5bTVs2rdRCaiGAU3wc5hLwSElEnGsOnBv6GI5Jz03Y03X5HgD/6/NQF+AlbNOE2mG1azOemA+X/OWS8sbWxKUETACo27nyHSHgXSZcUaqXPm1WmHamc1QURckkVcRWFEXJE67rviXpkuxDmbkPEW2SEdwry7I8Xa7chT5o6+Q+CvhWcbsBwDfjSIholWEYbxPRlz1OWlG+B4iIhRCvMPNJEsKfycyXE1GLhNieMXMfIcQ5svMIChG9wswyithHp1KpH8ViMemL2LrCzCSEiMvOIyBHZvqERLShoKBgjZ9jyho+qWRgpLdbc4IdnL2ldmjgoy/6VNb3t8H3ka/5xmwmy4fdDaJQhs1Xr6gus0TrQhCdAs+zuuGCUNeqW5fPkTBHump51S9dF/fA+/LOrRwGahzhXls3vK4xgNQ8iz8XP6S1tbWWgEORzvgQwAJ4oevQDbXHhT+HXFEUJR3pPNkpiqIoWaigoGAtgAYJoXXLsmQUH3whoox/UG5XjrYFYeMA1DLzMiHEF5ZlfWxZ1m0BxVSUfCOrG3pHIUTWzxm2bXs8vI84yEW+Rz9kCBHReEmxPXMcZxiAn8nOIwhBvDYz80t+bt/74qcPZ+KrQJ4KsC6IpzfOG/Z6mul51j++qNDR8QABA7weQ8D8ZHnhbJjh7PaIL4oXkk03thewvdYWXGbUNbU0XypjEWL1suoRGtMTAPktYDexhgvKjLLpMgvY4/4+LhJ/9uIqMC8m4DCkV9NZrYFPLDXK46qArShKLlGd2IqiKHmCiDiVSr1BREMlhD8DwMMS4nrS3sUW9izRPaC+LFYUT5hZ5kiPODPfl60LXZl5xzzuwgUAGIbxuhBCAIhICH8WM19DRF9JiN0tZtaFENfIziMorusODGAmvucvRXpNXrqzJpzb4GkmOzGAe5I2Bd/lPO7vkUb+4haCdhTgJRQx2H08UV44GWZFKCMhxj01rggF7mwGziV4vhMdBuoc27lk4QkLQ529PHrRaH33HfqNZRc3ASj1dTBhrUZ81uxj5v4F5OkOCcSUxeN3cRqitzFwPDit95gpItwRjaWumHHkLTIaXxRFUXpEFbEVRVHyCBE9AyD0IjYRHdfc3LxbUVHRhrBje2Hb9q/QVlQOFTM/F3ZMRclFkUjkTSFEEn4LC5lxkBDiTAD3SIjdLSHETLSNM8pbRNRkWdbrAAZKCF8ihLgSwMUSYndLCHEBgF/IziMIra2t+xHRrpk+r+dO7HF/j+jiixkg8tbpTPiHo9N01A4Ptvg6epFeWvzlJDCdDrDH4rD7EmvaOJgVrYHm1m7y0snFLtm1bQVsz2wQzy3bVH6pOca0gsvuu+KL4oVcxtPZwTQQol6PYzAT6GXdMc6aNXTWf4LMsSujF43W+/fpP9gG3w5Gf89DW9oxmInoPRBNnHNMzTKZhXhFUZSeUB1iiqIoecR13WckhdYjkci5kmJ3i5nPkBE2Go2ukBBXUXIOEQkAiyXGv46Zs25pnhDiaAC/k51HGIjoTxLDX9Da2rq/xPgdam5u7g/gOtl5BEXTtCDGfH0VjUbf83A7Kiv68nQQ/Rbe5jh/5bJbtWVW4HOwqbRfr1PBuBweG84I/C4cnNNYc1wou0nG3TYu4ur2TCb4ed/nAFRTKqGAPXnp5GKUYy4RLvNTwAbgEPCQHXNOkVnAnvry1N67l+92BcCPg9Hf/xm4FUwL2cbgmmNrnlUFbEVRcpkqYiuKouSRgoKCDwGslhGbmauZOeu6BZm5N4DTJYR+l4g+lxBXUXISMz8hMfZuQoh5suJ3hJn7AvgDvC9Ky2m2bT8mMXxE07QHmNlPgStQzBwxDOMhAGWycwlQEEsd/0IeRn30iS85kIFrAcQ8nNYmxg2ba4e/0vMMu1ZStfgQJpoDwNOXaszY6JJ2TrJuuK9Fluka9/dxkeJ9Ck1mXOjjMBfgOU3lTZeF3oG9JN7H0Zx7AT4P/mofFgMz0KCdW3dUnbRF3VXPV+0vWlJ/ZOByMArTOMU6Bp3e/J/mSjX7WlGUfKCK2IqiKPmnXlLcMsuyJkmK3SkhxLkASsKOy8zSukoVJRdFo9ElAEK5FL4TY1Op1GkS43+DmaNCiIeZeTfZuYSlsLBwLYCVElM42LKs6yXG/xYhRA3kjFcJUxBLHbudh91v3FNFjss3Ah67WhlPJcr3mN/T3LrTp7K+P5F2GwH9PB7SpJE2obHmuDcCTaydaZpaUUPRpWCaCh9LHEF0ky3cKxYevFAEmd/2Ji4dvzt0fpzhjvJzHIGSzJhQZpSZNWNqWoLKryumaWrxZfGTNIfqARoOQPd5ChdEj9jCOaZ2cO3jC88P92evKIoSFFXEVhRFyTNEJPOS/HgqlTpAVvzttY8HmCYjNhE9IiOuouQqItpCRM9KzuEOIcRhMnNgZsO27QcBHC0zDxmI6HHJ8SdZliV9fIsQ4ioAE2TnEaSmpqZ+APYJ4NTdzMNmaio2LgbhOI/nW+8a9jiYA4LtIK6sjzka3Q7gpx6PcMl1L0uU/fXPQaa1lWmaWuPARDURpsH7Xi2XGTNty76qbnhdqEscq5dVH8Ba5EkQjvKxdBIAPmXCb2oG19xpVpihLMjc3uhFo/XGgYnrAH4YwN5pnCJBxONL9dIz64bXhdKhryiKEha12FFRFCXPGIbxnG3b65k5jbl5PVZIRA8w82FEFOolox2xLGsqEfWVEHp1NBr9h4S4ipLTHMe5U9O0EyWmUMTMT6dSqaNjsdi7YQdn5ogQ4j4Ap4QdOxvYtn2PrutXwH/XYaYQgIWpVGpTLBZ7UkYCQohLmdmUETtMhmEEMQ+7MRKJ/LOrG5TGl/0CjCvgpZmL0EpEZ26efcJXmUqwQ6aplSQwE/BcWGdiWpCYN2JukGltZZqm1nhEwzlg7feAx5E7BMGMG5v7NF8dcgc2TVw28VAXzgMA7eN5+jOBibHKdt3T5g2d924NagJNsjMTX5y4O1vuXcwYlMbhLohe03Xn/FkV80J//VIURQmD6sRWFEXJM0TkuK57t8QUft5+GbRUqVTqQCK6REZsZlZd2IqShlgsthjAJ5LT6ENEL4Tdkc3M5UKIZwD8Jsy42aSwsPBjZpa1oHgrg4gesSwr1IXAzGxYlnULM2fNSJMgEVEQo0ReISKnsz/fceqTvcHOHQCKPJzOJfCNiZKhL2Yuw46VJA45jUBVXm9PzIsT5bEpQea0FTNT4vDE6QyqYa8FbMCBy7c1l4dewMbEJRf/L8P9E4H8dPm7YH7BAY+YN1RO8Xf0otF6/Nn4ENdyVwD+C9jM3Ayi2bqjD1EFbEVR8pkqYiuKouQh13XvBOBKTGG8EOJSWcGZuRcRPQz42kKfSQ9JiqsoOY2IHCK6XXYeAHZg5uWpVCqUgrJlWT8XQvwVwLFhxMtmuq7fKjsHABEA9wohrmLmwLvCm5ub+wshlgK4IOhYWSTjRWxN0zqfh81MQkQuA3CQp5MxXgSnamBSoO+lelfXH0HQvHdUM97UXFwIsyKU/QGTllWfQIRaeFw0CcAlYGFTqmVa2AXs6mXVo1ijxwD4uQLPJeDh1pR1ytzBc9cFlVtX4ovihbuV7xYH8SOUxogdBq/RNPy6ZlDN1FlDZzUFkaOiKEq2UEVsRVGUPNTezSZ1tiwzXyeEmC4hbsS27QcA/Djs2O1eicVi70iKrSg5z7KsOwFkwxKqIiJ6yLKsu5k5kOWwzBwTQlwJ4DUA+wcRI9four4EwFrZeQAgZjaFEM+1tLTsEUQAZibLss40DOMdAMcEESMbMXM5gnmN7nQedunFzwwG03i0jYzpGuMz0nlSovbkRCaT216fiUt310ALAN7B4yEbwNr5m+qGrw8yr62qllYdykR3Aejj9Rhm3M7FNGnhCQubA0ztW0zT1KqfrT6NgDtBVO75QIYL8FwupnNuOf6WhgBT7FTV8qq+3IcXEHAD/C8hdwA8brjO4DmD5j4dQHqKoihZRxWxFUVR8tdtkuMTM9/Qfnl0JIyAzFxg2/ZDzHxSGPE6yeFmWbEVJR8UFxdvBHCf7Dy2cbYQ4kPLssZlqiuXmXXLss4SQnzAzFejrfNXQVs3PoBZsvPYxtG6rv8rlUpdw8y9MnVSIcQgIcQbAO4FUJap8+YCx3GOQOY/h7YYhvG3jv6g1+SlO0PHLAC9PZzHZvD1idnDupyt3VP9zKeKHMepAfATj4c0gRFPzhsSyr6Nquer9ifCowC8FtgB4ifK+pRNrDm8piW4zL7NNE0teWRyPGm4Df7+HaUAvqLp3y3Twsx3W5OWTvqRxlRPjLPg/zWgiZgv013jzFlDb/5PEPkpiqJkI1XEVhRFyVPRaPTPAN6WnQeAC4QQr6VSqQODDNLS0vIDIcRfmFnaQjRm/jQajT4uK76i5AvHca4DIH057Db6ArhNCLFGCDGdmXdN5yStra17p1Ip07bttQDuAbBnBnPMG5FI5A4AUi7t70QREV0hhFhnWdbsVCr1o3ROwsw7WZZVaVnWSmZeBuDgDOeZExzHCWKp4+sdLpQ2WTMc9zKAPHZ+858aW3a+DUReVwL6Z5ralgZ9MohOhpfO8LaO2+uT5a8/BgSYV7uqZVV7aA7+RER+FoQ/GSmInWUebIbXgc2m1jiw4SIw3wj29AXFVk0Erlyf2Dhz4fnhjjwBADCoannV8a7mvAjg5/D2GNj2+NUgGlbyavlNanyIoijfN4bsBBRFUZRgEJHb2tp6haZpf5adC4CfE9GbqVSqLhqNziCiTZk6MTNHhRDjAVwLIGNdcunQNO22Dj9EK4riS2Fh4VrLsu5C9s0I3pOZbxBCXG9Z1lvMvBzA20T0vm3bXxQWFm4G0AqgOJVKleu6vgcz7w/gp2hb1rUvADAHXofKaUSUsizresi/omh75QAmEtFEy7JWA1gC4J9E9J5t2xsLCgoaAWwB0Ku1tbVU1/VdABwAYACACiHEz6CaiEBER2X6nMzc4SiR3g31w5m08+CtUPgJ6TwZCw8OsLDJVJZceoILng5PjwViAv8x0bxuFmrNwHedTFkxZRfbth4GyOsXNQzw87Zwz6sZfOPmQJPbRmV9ZSy5rGEaiC6Dv/0nXwJ03pzBtU8GlVtX4q/GC+k5rgboSgYX+DzcAeMJLaJfOLti9leBJKgoipLlVBFbURQljxUUFDxlWdZfARwmOxcABUQ0RQhxXvuM2TtjsdiqdE/GzGVCiDOEEBMB7JW5NNPWYBiG9+VMiqJ0ybbtGwzD+B2AmOxcOizS2OcAACAASURBVEBo+3Lu51t/wzAMCPHf2pemaapY3QORSORuIcQ0AHvLzqUT+wK4CGj7UkLX9W/d/7oe+D7InMTMRUKIn3d/S380TftOEbvX5KU7a7Z7HYBCD6cQRKhK/D97dx5eR1m2Afx+5szMSdIladlkUQFZxeVDUJFNS1cQRNRWUZaiEramTdpSFhFG2WmapWGtgMCHiC2LCBTapkQREBE/2VettNCyFJpz0jbJmZkzz/dHTrDUtpnQzMxJcv+uK9cF6bznvZNJTs555p3nnfPN5X2d7SOZqh/cJwhS14ogZAFT/6a5fBXmnRb5iuFz/3zuiFxH5zyIHNSLYU9qXk5sOqppdWTBNuK86NjZt7O/gOrZ6N1FoRUixuS6MXUtUWXbkpmLZm6fX+dfpYIfAdrbOkwbVK8st0Zc5Yxy/EgCEhH1A4N+JQAR0UAnXatUikkFgBoRecF13Wdd163r7Ow8prOzc/ct9ZtV1VLXdb/iuu6UXC73gOd5qwDMRXEUsCEitSIS6SZQRINJWVnZm6p6ZdI5KBki4qnqjKRzUN/yff8g9G7lbBieaZpPfuQzE+enUnmdha67IHqkwB2Z8r9Gujp3uzNbhhpI3SCCncKN0HfzyE/OXnd05JsO1jxRU5rr6Jitokf3YtgzvpE/sWFCw9uRBdtI5dOVVvbt7C97X8DWV4KUTkqqgD1j0ZR98oZ/FwQno/cLCV8XNY4vf2LEFSxgE9Fgx5XYREQDnGVZLblc7kER+WbSWTbhCwC+YBhGDQB4nue6rrsSXbdjt6Pr75QNYAfP87bvHiTSu/aBMXjPNM25SYcgGmhs277c87zjAeyZdBaKXzqd/r3nefcluVkv9bko+mH/XUQ+0hu4fOehX4fqmaFGK/7ti5wHJ9p2HTm74xKBhPr6VdFpCE5f13D0y1FmAro2R2xrz/xcRSZL+P7Mr6eC4MT6sU3/ijTcBpz5jp1pzVwKaA16U8AWvJoP9Ftzj5j7enTpNm9689RReZVfCfCZXg4NoPqwGKmaurF1r0USjoion+FKbCKiQSCfz58OoD+sErYB7Abg8wC+CuCAwn9vv6VBReBiEVmXdAiigUZEOkXkDADsyzFIeZ5Xha4LmzQAqGqfF7E37oe9S80TpQKZi1BtRMQT0entDRMiXU1cXvPwJIGE7/Evcllm5br7I4z0obZDWn+iwEwAYXvgfIC8nFA7fu4LUeba0MT5E+3syNbLBKhG+JwA9ElNYcLccQkVsBdXT1Y17hVIbwvYvgJ1Vln6B3VjWMAmIurGIjYR0SBQVlb2FoCapHMMUE9ZlnVd0iGIBirLspYC+N+kc1AyysrK3gRwQdI5aOupqgWgN/2WQ0mlUhsWsWVtkLlAuzbTDBEKN2Xa33ywrzNtaNiMRfuoagPC9vdXvXtouzcHCyblo8wFANVLqr+pkCYorJBD1kLkB/UT6p+KNNgGJrdMLtllxE5XQKUavbuT/HHNy3caRjW8EVG0zXJanJLqxdWXqeAGAOW9HJ6ByIkVj1ecc9Wh8W2WSUTUH7CdCBHRIGHb9i25XO57RdpWpL/yVPVUEYn8jSbRYGZZ1hTP874KYO+ksxSBLHpfFOnXLMua6/v+aFU9JuksRaANwDCEb/tQNAobOg7p44cNUqnU493/M3z6wq8ikKlhBoriJTNtXIzG6DZNLD/jgRHIB9cB2DHUANUXDOSnr5p3THtUmbpVN1d/TRQ3I/zmuesD0VMbxzQsjTLXhirvrywb4pVeDpEpCL8ALwBwfyB6WuOExncjjLdJ05qn7ZD1W+sgOB7oRf87gQJ4XgWnNIyu/7/oEhIR9V9ciU1ENIjk8/nTAHyQdI6BQlVr0+n0c0nnIBroRGStqn4XwPoeDx7Y2lQ1fEuCAUJE1DTNkwEsSzpLwoIgCE5EV5Gu3xEJ1w+6l57r3lR5uzPnD5VALgcwNMS4digueP+qcasiyNTFaTFhG+cA+HrIEWsgmNbaeMyKyDIVTHto2t6ieiNCtmsTSKcC540YPWIBYmrvVDO/pnRI+mMUsFXvVhOTG8fEX8CuaanZw1C5E5AfSm8K2EAeqvf4qfw3G0Y3sIBNRLQZLGITEQ0iZWVlK0XkOAC5pLMMAH+zbfsXSYcgGizS6fSLqvrTpHMkKAiC4AQRiXyjt2IkIq2qehyAjqSzJEVELiopKflD0jm2wuERPOafuv/DSw8ZDeCQnoeIKnRBpnO7ByLI8+EkFZmOoyFShXCr5n2FOtmGox6JMBMAoGph1XaGKdcD8tmQQ3xF0LCydeUNjkS7+WU3Z75jY0RwVS8L2ABwly0lpzaMaoh9H5gZj0w9AL7+AcA3ejVQkQN0tlWaPqVpVNNbkYQjIhogWMQmIhpkLMv6M4BBt5Kvj7Xm8/nviwgvBhDFKJ1O3wlgUPagF5ELS0pKYtnorVgV7nwJ1SpioBGR35umeWnSOT4uVRVVPTiCx/3zh//tywsAWkOMWm7mcQHmHRhdG5Epf9hVIXMAlIU5XgV3trVvf31Uebo5LU6JaRt1kNCrwwOF3lFujvjFgkkL3EjDFVQtrEpnR7TOAeRM9KZeobgnZ7o/uXLsldno0m3a9EemHxXkjQcB7NubcSLSqsCp60d0XMj+10REPWMRm4hoELJt+xZVrU06Rz8VGIbxo9LS0n8nHYRoMLIsqxrAkqRzxElE7jJN87KkcxQD27ZvBNCQdI6YPWea5okiEksbhyi4rrsfgG36+GHVtu0Pi9jZpiOXAbh3yyOQV+gFa5qOim7F68T5KTGtJgC7hxzxT0M7q6IsqgOAo47R5mXOh8rx0NA91Zvt9vQUZ5TTGWW2bo46hmkb5wNyOnpXwL6r3HJPvnbUteuiS7dpNYum/UTzwW8B7NDLoW/m88GRDeMabp934LxIzz0R0UDBIjYR0SBl2/Y5InJP0jn6GxE5xzTNh5LOQTRYiYhrWdZ3APwt6SxxUNUHTdM8oT8XMPuaZVnTAdyedI6YvGxZ1jgRib0415ci6of9qoi8t8EsaiiuBbDZ75UKHmhrOOo3EWT5UPnOQ2sUCLuJdpvm9cRMw3GRt7/INmd/EIieDyAVcsjzvpc/4apj41kh7MyfaGcfyf4CKucDMEMOC1Tx25Sak52YC9hVC6vSNc01v4Ah1wEYHnacQlWhjwXQQxvHN/4VMfUYJyIaCFjEJiIapEQkME3z+wD+N+ks/YWqXmJZFlewEyVMRNZZljUOwFNJZ4mSqi62bft7bF30USKilmVNxsAvZL/med4YEYl9g7oIRNoPu1tr45HPAVi8meM/MIDqCHJ8aPi0h74MiBPycF+gF7XNPfKvUWYCgOrm6q8BOk8gYQvYKxT4ftNRTasjDVbgtDhm2zY7n41Az0X4AnYeInfYHfZpteNrY930d9Zjs4aZllEH1fMBWL0Y6gvk9jQ6jm4c2xj5Bp5ERAMNi9hERIOYiPiWZZ0C4Jaks/QD16TT6Z8nHYKIuohIxrKs8RighWxVXWTb9rEiEstt/D2IZTO33hCRfKGQHemq2gS9ZlnWN4YMGbIq6SDog/MvIof2RZANbdgP+yNSxmUA/I8eDFUVJ9Nw5PK+ztGt/NwHRoiB6wAMCTVAcddQqbgBEd9lUfNwzR6iuCV0LuADiPykYWxDLJvITpw/MZX11pylAX6GXhWwcbvv+mfFtVK821nNZ23jdbg3KnAGwueFAu1QvRhD5LQrx86LvW83EdFAwCI2EdEgVygE/BjAtUlnKWINlmUNys3EiIpZoZB9hIj8IeksfUlE7rZt+7giKWADQCwbuvVW4e/XSap6VdJZ+tjfPc8bJSJvJx2kYKvOf0dHx26quktfhekWBMEmi9jZOeP/DsgDH/mk4E9pL30LomrdMHF+SjqNi6DYP8zhongJlnH+W/UHd0SSp+DcP587AilcD2CvkEM6RDGzbnTd0ihzbWjnip1PgcgVAEpDDlEobrfW21VNRzW1RZltY2e3nP0JW627AUwSSNi+4gCQgeKscmvEZfUH10d6zomIBrLQVw6JiGjgEhFV1Sm+769Q1UvAvw/dAgAzbduuTzoIEW2aiKxX1e94nncVgBog9IZlxSgQkQtN07ysyHpgF+2mYyISADjHdd3XAVwNIJ1wpK11u2VZlbZtF1Oha6vOfyqViqIf9hulpaWbb8cgxqXQ4JuAWgDWBtALVl87KrKeyRU7DfumAj9BmEViinWBGDPbasdHukG0M9+xs52tl0BlVJhnRYXmRTB7uDni9rief2qW1EwCtAGQktCDVH+zPtdx5rxjG9ojjPZfpjdP38v3vNsg+Govh74nYvygfkzdHyHsf01EtDW4EpuIiAB82GP0ShEZo6rFsvorSe2q+n0WsImKn4jkbdueoaoTAcS6Mq8PZQ3D+JZlWZcWWQEbqlqUK7E3ZNv2jQAOARBpYTBCPoDptm2fKCLFVMAGtn4lfhT9sB/d0j9my61nAHRtXq06b+3KdU9GkAEAsE3N/TuraC2AoSEODwCZ21Y/7uGo8gCA4zhGdmT2DEAqISHf86vc0ZrKXu6McvyeD9560xdPH63QmxC+zYkqcFu5lT113jHzYi1g1yyp+bxqcP/HKGC/Gvh6eN2YupakCtjOfMdOYl4ioiiwiE1ERB9hWdafbNv+EoCWpLMk6B+qekA6nb4r6SBEFF46nb47n89/EUBst8L3kSeCIPiKaZoPJh1kM4p2JfaGbNv+u2VZ+wO4CVG1jYjGv0RkbLFeNO2DixhRrMTeYhEbzijfUL0MkL96YszBgkn5CDIAVQvTeTUvBWTPcAPksSEjtr006j7Y2YM/GAPoxQh5Z50qHk1Le9Uto26JpYVR9ZLqb6iRXyDhCv9QqIrIXdIqpzsxZSxMLDXNNYcBwWKEb8kChSqgS03T+kbjkY2vRphwsxzHMWqaa8a0VWR+mMT8RERRYBGbiIj+i4i8Y1nWWBGZBSCy22+LUKCqsy3LOiidTr+SdBgi6r3S0tI3LMsaC6ASwOqk8/QgA+B0y7IOKykpeS3pMJtjGEbRr8TuJiJZ27Z/KiITACRSPOoFT1Uvtyzr85Zl/THpMJtjGMbHvoihqjsACFngDW9z/bA31Lpq7YspQya2N0yI6u4yGW7IRAV+FPL4NYEvp61yDox0FfGURVN2g5G6AYphIYcsg4WT49pssGZpzVcEuAUqI0IOCQC5T32cXj8pvn7SjuMY05dMO0Y1uBOQT/RiqG9A7kDemDR71Ox3Igu4BTVP1JS2HZqpBrTBUHNBEhmIiKLAIjYREW1SYcOs2b7v7wtgftJ5YvA3ETk0nU7PEpF+U7Ahov8mImrb9q8sy9oLQB2AYmvPoADutCxrX9u2byj0dS5m/e450bKsxZZlfV5EZqA4L2Y8pqpfSqfT5xdh+5CP2JqV2K7rHoY+7lOvqu+EuuizYFJ+Td34N/ty7g1VzHjwUyJ6BcKtdg6geu7aq8dHeoG88unKMitl3QTFriGHtCElP24Y1fBGhLE+VL2kel8N9CYAn+7FsIc88X5aP6F+TVS5NkHaDmo9TkVuFchOvRjnQaSxs9U9Pea8H5rWPG0HtGOeKq5AXmbXjq9dn0QOIqIosIhNRERbVFZW9pZt298XkbEAXko6T18TkVUAJluW9VXLsv6SdB4i6jsikrFte4ZlWbsDmAMglpWGW+ACuFVVP2/b9vEiksgqvY+hX7QT2ZiIeJZl1VmWtZuIzBSRtxKOpKr6sIgcYdv2Yel0+oWE84T1sYvYItLnrUREZMutROLgqKG+zAFk55Aj7smOWHdrtJEcY0im7DKojgo5xBXozPpR9bF8P2e1VO0iwK8F+FzYMap4FIacds2Yaz6IMtvGpi+uPllTcjOAil4My6nggvI15edfO+naRO5irF5c/T8SyH2A/kgEf0khxbZ4RDSgsIhNREShWJbVbFnW51X1OwAGQrF3GYBppmnuZdv2rcW2kRoR9R0Rece27ZmWZe0M4DQAf0W8PZNXq2qt7/ufsW17cjqdfjHGubdaf9jYcUtEZL1lWXNM09xNVb+rqg8h3sL8egC3qer/pNPpIy3L6m97TmzN9yqKftg9thKJ2vDMQ8dD5NvhjtblMI1ZcCZF+nuUXbzmRChODz1AcN3w1hG3xrHh4Kz7Zg1zPeNmoFcbIz6nhk6qH12/MqpcmzK9ufosNXAtgOGhBwk6VHF6xeiKWmeSk8TzpVQvqv62GPqQdG0+2ZkX/TlXYRPRQBNqowciIiIAKNzyfi+Aez3POxTA2ap6NPrXRdHHVbXBtu17RSSajZ6IqCiJyHoA8wDM6+jo2NU0ze8GQTCusFq0tI+newPA70Xk96ZpPtbPn28GRCFERHwA9wC4R1W38TzvOBGZoKpHAAjbnzes9wHcHwTB79Pp9JI+bhnSp+05QvhYq0pVtdzzvC/0dRhVTXQldvmURbspgtkAUiEOd6E4J1s7/t9RZpq6aOoBasgcAdKhBqgu9L3gwjgKrlULq9Ku5f5KIGPDj9JXAuCYxjGN70aX7KMcxzHaDm2doqpXAlISeqBoqxiYXD+m8X4ksKGs0+KY2Xx2FlQvhEoaADTAzasyqx6POwsRUdRYxCYioo/FsqzHADy2fv36nSzL+h6AiQAORnEWtF8Qkd/m8/k7S0pKliUdhoiSV1pa+ga6WozMUdUS3/f3V9UDAPwPgD1FZA9V3RHhCoYrVPV5AM8DeBbAs+l0+uWIolsRPe5mBUHwftxzRk1EPgBwI4AbVTXluu5+InIggC8B2AvAHgA+iXDvl94H8Fzh43kAz1qW9UwUFy5U1fA8L9a/s4XvVa/l8/mDEa7Q2xtrbNtOrg1L5dMWzNW1AuzY47EKhchN2REld0cZ6eyWsz/he+4NgGwTaoDiRSPln9k07pq2KHMBgDPfsbNWa61CJ/Xi2suyfKAT546fuyLKbBty5jt2dkT2LARyOSTkhYAubwaKHzYe0fhYZOG2YOaimdtn/UwtujYXNQBAgdcNNa5cMGlBf75wSkS0SSxib4IGOCmV0r5ejbNZQaD9os+gKf6dgRhPxzlnPrCei3O+jZX4H2Q8e1gvVg1sPdXUgHujSAPbkCFDVgGYC2Bue3v7zqZpfk9Vx4rI1wCMTCjWGhF5VFX/qKpL0ul0v+/lLSKTAZTFOKUf41xhrSv0Zo+NqiayMdOWqGqTYRj3xjmnaZqRbkaWNBHpRFebpI+0SlJVA8C2rutuKyKlAEwRMYMgyAZB0FZSUtImIpmYs1qq8S72S6VSsfajjVuh2NxdhL55w39T1ZG5XG47wzCGFo4doqpt+Xw+W1JSkgGQjXljztgvYqjqxy1iHybSt4vGReSxJDdCrSh9/3gFvhnmWBG85AIXwxkV2d/TyS2TS/y8fwlEDghzvEI/MEWm1I6+ZnlUmbo5LY7Zls/MgMppErKCrdBVGuCHc8fPje1CxeSWySUZL3OhADMgsEMPVLwYqP6kcXzjXyOMt1nVS6r39dW/XgSHQQvfX4VriF5SN74usg1NiYiSFPetaERENEioqriuu0+hmH0IgP0B7Io+vmVbVd8RkRcAvICujSefsizr+STf5BIRRcXzvMNV9U9xzhkEwd4lJSWvxTknbZqqDvM8L/IVtBvNeXw6nb6zt+Nc1/0zgEP7MouInG1ZVm1fPmZY5TUP7I4gtRiCz/R8tHYYmprU2jj+gajyqKrMWFpdqSpzgVDFV19Ep9SNabwhqkwfUsj05mknKuRaAENCjloNlRPqx9UvQUxtOSqfrrSGZsouUcV09GaBn+BZqJxYP7b++ejSbZrjOEbbwWtGKYzrIdjjo/+qd5ebI05wRjmdceciIooDV2ITEVEkChslvlz4+HBlm6pWeJ63q6ruKiK7ikh5EASlIlIOIC0iQ9HV0zGnqnkAbV3DNGMYxgeFovU7QRC8bdv2ShFpTeLrIyJKSOwrcdPp9IBeid3PxH7+DcPo9flX1VSh7cnf+zKLqjb35eOF5sy3kTF/BsHuPddXRSH4devK7ENRRpq5tPpADeSy8KuH5ddvrll5Y5SZutUsrRmtonOgoQvY6wGZUv5EeTNiKmBPnD8xNSRTenmgWi2QXrS90ScNMX8wZ8ycyFezb8xxHCN7SPZkwJgNYOP2MSvzquexgE1EAxmL2EREFKvCrffPFD6IiKgXgiAY1tctGnqQB8CLhUWis7NzaCrV122mt0xVV/d2TKFFyyERxEnE8MzwsYCegFB3Muu/0Zm/AAsmRdaTuOrJquG61rgFomHbtv3VKrVmLBgbfZ/kGc1Vnw1UbwawbcghngBnD3+8/C7HcWK5i27i/ImpnUfufBUU06Q3fdtF/p7Km8fWjq19L8J4mzRx/sRUdmT2Qqiei/9eeR8gkJ/NHT/39bhzERHFiUVsIiIiIqJ+QkT6tCVTCGvYnql4pFKpuM8/fN8f1Pu1DJvRsq3kO+sQpmWHIg8Yp2evOzKyCz8T509MWWtT1yr0s+FG6DtBCidfdehVa6PK1K360eodgxzuQtemqGEEgP6ybmzjdVHm2lDVwqp0ykpdIQGmQkJvRq4K3O/B/fE146+J/c6U6pbqCvH1Gqgej01dSBG9o358w61x5yIiilusO1sTEREREdFWqYh5PrYSKSIJXMRAaWlp0W1wGyfxO88HsFe4o/Xm7KrsI1Hm2WXkTpMV+EHIwzsCQXXjEY2R97SfuWjmEMnp/wLYN+QQVcGv3mpddXmUuTZU+XSlZZrG+QJU9aKAHQC4N430SdeMib+AXdNcs7v4ehdENl3Ahr6Sylsz4s5FRJQErsQmIiIiIuonVHWbONuJqGrsfV9p84IgGBlzO5nVItIe54TFZPi0B78qgjPDHa2vW2nTibKNyNRFUw+AymUI1wIjL4KGjn913IOI+0xXLaxK5w2/DpAjQg9S3G2X2mcvmBR9ixOgq4Bd1lo2C4JzEL6FSACRu9Pp9GlXHHZFNsp8myDTmqcdCNUbAfnCZs5gNq86pX58/O1NiIiSwCI2ERERERUF13XPBGAGQfCGYRjLLctaXuijTwUislvM80W+grNbLpf7tmEYnwmCYLmIrCic/3fjmr8/MAxjN9VY9r3rFtv5LzY7Vd5ftl6MenRtNt2THEQveP+qcauiynPGA2eMMAyjAcD2YY5X1UeQN2rnnTbPiypTYR6ZsaS6WgWnQMP0DAcA/FlLMDWOFicA4LQ4ZmZN5lwR/AzhzicUqiJGdwE73n0BFDJ1ydQjDZXrsfnWLL4qLns78/YfY0xGRJQoFrGJiIiIqFicAuBAw+i6y9vzPLiu2wlgFYBlAJaJyDJVfVtEVuXz+WXpdPqNQdazec+Y53s1rolE5EhVrexeaVw4/zkAK9F1/t9W1VUiskxElhXO/woR8ePKmDRVjfv8vxLzfEWjvSxVCeArIQ+/J+vLfVFlcVocM+u3ngfFwSHLxG+mDLN6zrg5kbeCqVla810R+RkUVqgBqq+Y0J/MPnzu2xFH+1DWbz1XRH4OhMwIQIB7bF1/6hWH1ce9Ahs1zVMnwzDqoFtsH3WPqeY1ca1kJyIqBixiExEREVGx2NQKuRIAuxc+0L0KVVVhGAY8z+t0XXc5gBUAlovIclVdLiJv+L6/vKSkZNVAKXKqquF5XsjevH0jzpXY2PT5T2OD899d4N7g/Huu664EsBzAclV9Q0SWi8iKfD6/vFDkzsX1BcRgnzgni/n8F43yqoWfAWQWQrWd0JUqxoVoOjKyn7Nsfs0YwDgrVB9nhQuRmXPGzHkpqjzdZiyaekAQYC4Ew0IOeQ+BcfLsCfWvRxrsP2T6kupTFLgIvap96K/Wd3ZU1x8zL95WOgqpXlJ9NoBLergo8LymUV17eO36uKIRERUDFrGJiIiIqFiEus17IyUA9i58fKTInUql4Hmev4ki5woRWb5BkbOzr76AKHme90UA5XHO6ft+bCux8fHOvwVg18LHporcmsvl3hGRNwCsKFzgWG4Yxop8Pv9v27ZXiEgsLQ22lqqWeJ4XdmVwnwiCYPAVsSfOt2HI5Qrs2PPB4kHwy7b6Cf+MKs5ZD571aahxDYCyUAMMbaof0zA/qjzdpi2Z9qkAxm2Ahvg+AQDWAlJZP6H+qUiDFTiOY2QPzlYqdC7C1z0CBRa0L+s4K+o2LBurvL+ybMjSIZcrgrMA2ezFE4WuMlKpE+sPr4ttJTsRUbFgEZuIiIiIikUUK6ZNAJ8ufGyqyIlcLve2iKwAsNz3/ZllZWVvRpBjq4nI2Jj7IbeXlJTE+b2I4vyLiOyIroLk17rPfxAEEJHuliVr0LWKfwWA2ZZlPR5Bjq3m+/6hAEpjnjbOixhFoWLnoUcpcHS4o4M/ZlvfvS2qLJVPV1p2q3UVCncihPDX1lT2gqjydJv12KxhbofbBOhnw40QVyS4oG5MQ2QtVzbkOI6R/dqaSRBjNsK2EFEEIvit1d52RsNpN8dawD6v+bxtOtHZANXjRTZfwAawHpCquiPqno0tHBFREen5diQiIiIiongksiK2UOT8KoBJqVTq8CQy9ERVU6p6WszTviIicVbNk1oRPRLA/qp6rKp+J6EMYZwZ83yubdv/innORO3qtJQojEqEu1iQBYxzccspkd3JMSRTehKA74U8fHUgWnnLqFsivbPEcRzDa/cuEeBb4UcF1wx/bMTV0aXagELaDmk9DqnUtQCGhhwTwNA7LLSfddWxN8f6PDSrpWqXTnT8Gqo/wpbb1wQCXNAwtuGeuLIRERUbFrGJiIiIqFisSzqAiByYdIZNcV33+wi/GrOv/CXOyVQ18fMPoCjPfy6X20dVj4152r+LiBvznIl6wxmVQ6BzoPigx4NVr8k2TPi/qLJMb56+F1SuQIj37KqaVwPnN45ufD6qPN0yB2d+DEPP6MWQ+1KB9XPHceLYgFdmLJ02RiHXQHVEyDGBGrjdHxqcdeXYebFu4nj20qrPqKuuyQAAIABJREFUuH7qt1AcA/SwZadq/ZutK5viSUZEVJxYxCYiIiKioiAirUlnADA+6QAbU9UhIvLLBOZ9Ms75DMMohvP/FQ1f/IrTHMT/3i3W818kNNs44RE1MAXAljb1+2cqwJVRhahaWJVWBNcB2DbM8WIYv80YmdshiPTOielLph8kgtk9bDr4IQWeMU3r9Nrx8WxAWP1w9f5dfbqxQ8ghCtE73JR7VtNBTW1RZttYTXPN7r6mHhTg0J6P1jtSal20YNKCfPTJiIiKF4vYRERERFQUimQTuX1d141187yeeJ53GYDPxD1vEASPxjxfMZz/Es/zJiUdYkOu654sIkfFPa+qxnr+i4aIttVN+J2qngNgUyvRg0CNmjVNR0VV9BTTNqdA8fVwh+tLVuCdE3Ubka6NHINbAFSEHLJSfT159qjZ70QY60NTF0/dUww8ANVPhB2j0MW+G/z02lHXxnoXSM3Sqq8AuhTatSHxZnVdlHgwnXOnxHUhgIiomLGITURERETF4uWkAxRMSzpAt1wudwKAKQlM/WJpaemKmOcslvM/RVXNpEMAQOGCShItBFzbtpcmMG9xENG2xqOuBuTnAD66+lX1zrUd2yyKaurq5uqDAPwMW+6P3BUFug5izLhq3NWrosoDdG3kaIhcLeih6PofGQVOazyy8bkoc3Wraa7ZPSXGfRDsGHKIAnjQNb3vNR3VlIsy24YcxzGmLZ12OILU76HYtYfDFYpHU0Hnj684+rpiuEuFiChxLGITERERUVEQkWIpYv7Q87zRSYfI5XLfE5FfI4HX7Kq6MO45bdv+JwAv7nk34XO+71cnHcJ13f0BLAQwLIHp/ywiSW20WTSyK9vmAFoPwO/6jLybD3Ap5h0Yyc9pzcM1Iw3gilD9nBWBiHFN+WPli6PI0s1pcUy33T0PAb4ZboS4UL2kfkx9LM8h1Y9W76iqtwLYtxfDliIvJ8W5AttxHKP1oNbjjEDmAz0U2wWqqn+GIT+qHX/9e/EkJCIqfixiExEREVFRsCzrWQCxbqy1Oap6h+u6X0xobjOXyzki8jsAiawIFpHfJjCnC+Bvcc+7Kap6aS6X+05S87uuexKAxwBsk1CEOxKat7gsmJTPVnz6Z1DUAnCh2rBu7pGRXGybOH9iSkw9SzVMj2QAgr9pSq+IdMNEhWS8zEQRTIOE2GASqgK96a3MqiYRibQ/NwCc13zeNsjhehEc0othfzJc76f1E+rXRBZsI6oqbYe0HWekZB5C9OsW4M8+8sfXj65fGUM8IqJ+g0VsIiIiIioKIuKLSEvSOQq2B/BInIXMQvH6eM/zXhKRi5Dca/W/27b9jyQmFpGHk5h3E2wRudPzvBmq2mNbh77ied4o13UfA3ArgLK45t3IWsuy5ic0d/Fx9nOz2Xd+AQRn5QO9CREVZ3cq3+lAVcxAuN/7dYL8GQ2jGjJRZOk2Y2nVvmJoHUL+LIriL+vQft6CSQs21Uu8T81cNHNIR9BRK8DRUEi4UfKPlG2eMueb1yyPNt1/qKpMXzztJEVwI4CRPQ4QecLImxOvjrhFDBFRfxTyyZ6IiIiIKHqu654G4Pqkc2zkERGZbZrmUhHp0zYCqmr6vv/VIAiONAxjsqru3JeP/zGdadv2dUlMXOgB/dck5t6CF1X1Ctu2fy8ifdp+QFXFdd3PG4YxTlVPAvD5vnz8j+lG27ZPTTpEERJ09VLuc06LU5LxM0sEYVdhy8/qx9RfFkWWbme2nDk07dt/AvClkEPehsih9WPql0WZCwAqn660hrSW/hIqs8KsEC941dTgmNnj5r4eabiN1DTXVEK1CYAd4vDHA9HvNo5pfDfqXERE/VFRbFhCRERERAQAlmXd53leEwAr6SwbOEJVj/A8b43ruvep6mIAL9m2/aqIhN4UTFVLXNfdXVX3SKVSewI41PO8IwAMFxGoRn73fRgdlmXF3kqkm2VZT3ue928AuyWVYRP2E5H/9Tyvw/O8Rar6AIDnLMt6pTd9owsr7T+dSqU+o6p7ADjIdd2xIvKJIjn33W5KOkCRiuwkZfKZs8MWsBX4Y8WO5bVRZQG6WpvYvt2I8AXs9Sngh7UxFLAdxzGya1qnQuTcXizJWxGITpo9Nr4CtqOOkV2aPR2qjei57hIAaPa9/AlNRzWtjiEeEVG/xCI2ERERERUNEXnHdd17AHw/6SybMBLAKSJyCgB4npd3XXcZgDUA1opIm6q2A7BExFbVYei6DT8tIjt4nrezdCmWgvWm3CEikbYo2BIRCTzPu0FVr0gqwxaUquq3AXwbADzPg+u6KwC8ByAjIusL5z8QkbSqVgBIo+tnYITneZ8yDMPc8NyLFN2Nsf+wbfvJpEMMJjUP1+wH1fNCHv6upGSKs58TXbsOhey8ZOdTBDgp5AgfkJ8Pfbz80cgybSB76JpJUOPyXgz5AIZMbBzd8FxkoTbitDhmpjlzhgCz0XPNJa+K+Xk/f3rTUU1tceQjIuqviu5VExERERENbp7nfUNVi6U39mDiBUGwT0lJSeSrKbdEVbf1PO9NACVJ5hiMDMM42jTNB5POMVjUzK8pxUg8CNVRIQ73VVFdP7b+2ig3TZy+ZPpBiuA+dO0L0BOF4DaskTPqJ9V3RJWp28wl1d/IK+6GhOgtDQCKNkFwfN24uQsjjvahyhsqraG7l52lwGUASns43AP06pzpXXjtqGv7tFUREdFAxI0diYiIiKiomKb5JwBPJZ1jEPpV0gVsABCR99G1sSHF60kWsOMlI4NToXp4uKP1YVPNW6IsYFctrNpOka9HuAI2VPGUmbLOjaOAPXXR1M/loTeHLmADHSJaNfyJkbFtFuu0OOaQ3YdMCVfA1k5VnOt7wXksYBMRhcOV2ERERERUdDzP+5qqPg6+Xo1Lp+/7e5aVlb2VdBAAUNXtPc97HcDwpLMMFiIy2rKsR5LOMVhMe2Ta3kZelgIIs5nr+4L8uLqxTf+IKo8z37HbRmQaAujpglB9bt43gmDCnPFz/x5Vpm5VLVW7mH7qXgAHhhziQ+Tc8lR5ozPK8aPM1s1Rx2hrzkwF5HKF9nQXyRpRPb1ubONdkOh6rRMRDTRciU1ERERERceyrL+IyO+TzjFYqGptsRSwAUBE3hOR2UnnGCxUdSEL2PFx5ju24cslCFfAzkP1iroxTc9EmSkzMjNRgZ+GLGB7Ap0RSwF7YdVwy09dA8EBIYcEgNavr1g/N64CNgBpa85MVWB2iAL2a4bohLpxLGATEfUWN3YkIiIioqLk+35NKpUaBaAi6SwD3LO2bV+cdIiNmaY5x/O8HwDYL+ksA9yafD5fmXSIQUTWjsxMBHBsyBJmi+8HV0dZ8Jy6aOrnBGgAYIUboVfXjW28Lao83aoWVqVNy6xV6LfCfPUKVQFuLd9pxAX1+zV6UecDulqItPmZ6gB6VQ8XAHwAf0gF5hm142vfiyMbEdFAw5XYRERERFSUSktLl6vqqUnnGOByqnqSiLhJB9mYiHSo6vcBRN5vdzBT1bPKyspWJp1jsJi+aPougeJCaKiCccZHflbTUU25qPI4LWcOTRnGPCi2DTnkL2g1fhZVnm6OOoZlGtWA/iTsGBF52GpPT3P2c2J5Pqt8utJam89MV+CyHgrYWUB/sb6z/UQWsImIPj4WsYmIiIioaKXT6bsA/DrpHAOViFyUTqefSzrH5qTT6RcBzEo6xwD2u3Q6fWfSIQYLp8UxAyM4G8BeYY5XoLYpwj7YAJD1rZ8B+FqoPKqr80FQGcdGjmubM99WkYsQtmYheNZIpU666tir1kabrMvE+RNTQzOlPw5Uf4Etr2B/zYB+v/zxEZfNO2ZeexzZiIgGKrYTISIiIqKiZlnW6Z7nfQrA6KSzDDC/MU3zqqRD9MS27atd190LQFXSWQaYv1mW9dOkQwwmrW7rwYYhYb/nT5uB2RBlnprFNeMgOi1ko5JcypCahtGNL0aZCQCmL5m+f4DgBgCloQYI3lDF8XNGzXk/2mRdHHWMzJLMKYFqrUA21wM7gOh8Q8xz54yeszyOXEREAx1XYhMRERFRURMR17Ks7wCIdGOzwURVH7Qs6xQR6Rcbi1mWVS0idyWdYwD5p2VZR4vIuqSDDBbOfMc2DJmDcIXZHESm146vXR9VnlmLZ+0E0SZouEKxCm5c+6/2+VFvRlj9aPWOiuA3QOj2JmsQyOSGsQ0vR5nrQwrJLMmcJIIGgQzdzFHvi+iZ5akRp7CATUTUd7gSm4iIiIiKnoi0tbe3H22a5iMIeSs+bdZfbNv+vojEsvFZXxCRQFVP9DyvHMDYpPP0ZyLylu/7Y2zbZm/eGGW2yRwqAb4Y6mDB9W+teeuJqLI4LY6Z9VtnAxLuuVTwFw/eRfNOmxfpc8as+2YNc3PurwHsGzJXhyGYMmd0/aNR5trQtCXTjhFBE4AhG/+bquZFsCivWj13zNx/Rl3wJyIabLgSm4iIiIj6hbKyspWWZX0NQGTFnYFORO6yLGusiES2wjMqItJpWdbRAH6bdJZ+7Bnf9w8pLS3l6tCYNYxueEQNTADwDBTBFg59WX1cuWDSgnwkQRTSlm/9CUQmhhzxHkSqrxlzzQeR5ClwXnRsr8y7TIBxIYd4UL2o9oj6O4F4isXVS6uPNAy5E8CmVmCvFMM4s9wccezccXNfZwGbiKjvsYhNRERERP2GiKyxLGu8qj6cdJZ+RkXkItM0J/XHAna3QmuZEwA0Jp2lH/qtZVkHl5aWrkg6yGDVMLrhEeRlNKCXANjUSvicKi5umNDwdlQZapprPqcqP4ducTPCbj6gF9ePrn8qqjwA4DiOkV2Z/TGgpwKQHgcoAqje5HvB3LhaIlUvqj5CAvz6v9qvCDoU+A1EDq8fUz/PGeX4ceQhIhqMWMQmIiIion5FRNbZtn20iPwcAAsGPVDVd4Ig+LZlWb/sLz2wt0REAtu2q1X1hwAySefpB9oBTLcs60ci0pF0mMGufkL9mvqxjY6P/ASILACwQYsOfaDCqrg3qrmdp50yILgKwM6hBqjeW946Yl5UebplDst8A0ZwGYB0qAGCR3w/OKfpqKZctMm6zGie9mUx8GsAO2zw6QCKp6DB8e0j2k+pH1O/LI4sRESDWc9XOYmIiIiIipTrul8GcDvYJ3tTFMDtlmXViEikrQCS0tHR8alUKnUrgG8knaVI/SkIgsqSkpLXkg5C/61qYVU6ZaXGCPRiqOwCkdH1Y+ufj2IuVZWa5pppAtQCSIUYssJT/2tXj7t6VRR5uk1fNP2TagRLAewZcsjrvpk/omlU01tR5uo2s2Xq5/K+cTc2+BsjwCqF1rri3xZ1mxUiIvoPFrGJiIiIqF9T1SG+789Q1RkAhiedp0g8JyJVlmXFtuFZUlTV8DzvFAAXAfhk0nmKgYisDILg3HQ6fXvSWahns+6bNcwf5u9ed0Tds1HNMa152hcMyGNQDAtxeF5SxrfqjqhbGFUeoHuDycwShL8IlVUDRzSMbvi/CGN9qLqlelfx8HvIhxty5gRo8rx8Y9ORTSvZ95qIKF4sYhMRERHRgKCqI13XnSUiU4GN+pYOHs8CqLMs63YR2dLmcQOOqtqe500G8Et89Lb/wWQ5gHrLsm4Qkc6kw1BxcFoml2T8ikcF+HKIw1WAOcPHVJzjiBPZc4ijjpFd0lqPrufrMHKAnFQ/tn5+VJk2NPPxmdvn2737ADlIoetE5e5A9MLGsY3sKU9ElBAz6QBERERERH1BRNYAOFdVG3zfP1lVf4Lwt6j3ZwGAFhGptSxr0G54KSIugHmqeofneT8A8FMAX004VlyeAdBoWdZvRMTr8WgaPBSSbS6fKcCBoY4X/C0H74ooC9hQSGZJ5kcickbIEb4ILh2eKr8nskwbqFpYNdxv928Txd4AbjWCoL5ufNNzXHlNRJQsrsQmIiIiogFJVcX3/a+r6o8BHA1gRNKZ+tjrInKb7/u3lZaWcnXgJuRyuc+JyE8BfAcDr9XIuwDuAHCrbduRtaGg/m36kukHqQQPQVER4vA1YhjH1I2ueyLKTDMfmfbVfF7uQ7g7JlSAO4aPqKh0DnTao8wFAJX3V5YNKRniCLQkUNxcP7b+2YGwIS4R0UDAIjYRERERDXiqmvJ9/6AgCI4UkSMB7I/+91o4D+BpVV1qGMaDlmVFWmgaaHK53OcNwzhSVY8EcAgAK+lMH8NLAJYahvFwKpVaLCJ+0oGoeFW3VFeIhwcgOCTE4XkxcNHwIyouj3IV9pkPnvkJ27J/LxLuLgkB/qadclz9MfUro8q0oXOWnFPe7rVv23RU07/imI+IiMLrby/ciYiIiIi2mqpu4/v+gQAOAHCAqh4A4NMJx9pYG7qKlk+p6iO2bf9RRLJJhxoIVHWo7/v7F85798feAIxkk31EJ4CXATxTOP9LReTtpENR/1GzZNoFgDgAUj0frUvXo+O788bOi+w5ZnLL5JIKr+I6iJ4skDC1iLc1j6MbJsSzkSMRERU3FrGJiIiIiNC1MaTneZ9R1d0Nw9hNVXcHsBuAXQCMLHz05Z4yvoi8p6rvqerbIrJKRF4G8ILneS+VlZW92YdzUQ9UdWjh/O9mGMbuqrqbqu4mIp9W1W1EZCSAdF9OCWA1gPfQ1RrkbRF5OQiCl1T1xXQ6vUxE8n04Hw0i05qnfdlQeQjANj0dq9APBMao+rH1z0cWSCHTm6tPV6AJoYrq8ALRExvHNP4uskxERNSvsIhNRERERBSSqpZ3dnaOTKVS24pIOQAJgqC716xR+Fw3V1XXA4BhGBl0FS07giDI2La9WkTeizk+bSVVHdLZ2blNKpUaWShqG0EQDEehKCciFfjPe6xAVbMAYBjGWgA+un4mspZlvQdgNduBUBSqFlYNN63UHwB8vceDFYEKqhvGNjRFmWnakmmHGiILoRjW48ECheql9WMbfx5lJiIi6l/6ciUJEREREdGAVmjnkQXw76SzUPxEZD2A9QC4kSYVLSttfU41+AQUOfR094DgkfYR7ddHmadqYdV2BuTGUAXsLg+vH9HxyygzERFR/8OV2EREREREREQDSM3DNSPFyO8PMUapyCio7oOulkj/IdqqKoc0jG14OaoclU9XWkNay24CcGKoAaqvGClzwpzRc5ZHlYmIiPonFrGJiIiIiIiIBiinxSlZ4635ZEqwv6hxuAJfhmA/EVxYN7qhHgKNau6axVNPhxhNCHcXeFbEOK5uTF1LVHmIiKj/YjsRIiIiIiIiogHKGeV0AngdwOtQLJi5eGZZJzrLt23d9v1IC9hLa76CQC9GuLpDHqIXDx89/E9R5SEiov6NK7GJiIiIiIiIqM/MuH/GtlqSX6jAl3s8WKAI9He+H0xuOqopF0M8IiLqh7gSm4iIiIiIiIj6hOM4Rrak9XRAdgHw9kb/7AIaQLFGgfWGYawAdFUQSAML2EREtCVciU1EREREREREfUMhZzx4RoU53PyvRXPbbLNN1tnPcZOIRURERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERECZCkAxAREREREREREW2OqpZ5nrdvnHPm8/n3S0tLl8c5JxFtnpl0ACIiIiIiIiIios3xPG9vAE/HOWcqlfoVgMo45ySizTOSDkBEREREREREREREtDksYhMRERERERERERFR0WIRm4iIiIiIiIiIiIiKFovYRERERERERERERFS0WMQmIiIiIiIiIiIioqLFIjYRERERERERERERFS0WsYmIiIiIiIiIiIioaLGITURERERERERERERFi0VsIiIiIiIiIiIiIipaLGITERERERERERERUdFiEZuIiIiIiIiIiIiIihaL2ERERERERERERERUtFjEJiIiIiIiIiIiIqKixSI2ERERERERERERERUtFrGJiIiIiIiIiIiIqGixiE1ERERERERERERERYtFbCIiIiIiIiIiIiIqWixiExEREREREREREVHRYhGbiIiIiIiIiIiIiIoWi9hEREREREREREREVLRYxCYiIiIiIiIiIiKiosUiNhEREREREREREREVLRaxiYiIiIiIiIiIiKhosYhNREREREREREREREWLRWwiIiIiIiIiIiIiKlpm0gGIiIiIiIiIiIg2x7KsVb7vnxvztM/GPB8RERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERU/SToAxUtVR7iuu4OIDCl8ylLVdlVdl06nMwDaRaQzyYz08ahqCYAK13UrRKQcgA9A8/n8+yUlJe+KSC7hiIlS1REASnK5XKlhGBUALAC+qq5XVTedTrcCyIlIe7JJKW6qarW3t29nWdZ2AEx0/Wx4QRCsC4JgfWlp6XoRaU04Jn1Mqlre2dlZkUqlKgCUoOv3vtO27fdF5N2k8xUzVd02l8sNF5FSESlB1+vGFAA3CIJsPp/vLCsr6wDQJiL5ZNNSX1DV4QCsXC5XLiIlIlKKwnMiAARBkAGg6XR6HYDVIqIJxqWNFF4LlnZ2dlYYhlFaOH8mCr+z6XS6A0AngAzPXXFSVRvAyFwuVwYAhmEMQ9c5TAEwUPhdVNVc4T1cUFJSskZE2hILTVukqkM6OjpGGoZhAZDC+xCISImqfvi+OwiCdeh6/ZkrLS1t5XsSGgxU1QIwLJfLVRiGMRyADSAfBEE2CIKO0tLSdvBvVqJUdQiAslwuN6zwNymNrnOUSafTbQDWxllDZBF7gGpvb985lUp9DcBXAOwrIvsA+CS6fuB6shrAywBeEZFXATxrmuYTItIRXWLqrY6Ojk+nUqljARxY+NgbXS9uN2c1gFcAvADgqXw+/8fS0tI3Ig8aI1XdPpfLHZRKpfZS1T0AdH98Elv+3mzIB7ACwDIA/xKRZUEQ/AvAS+l0+uVIglMsVNX0PO8LAA4G8EUA+wDYE8AOIYb7AJaJyCtBELwiIq+q6l/4M1FcVDXl+/6oIAi+AeBAETkAwLZbGOICeFNEng+C4HnDMB4zTfNxEVkfS+CEqeoQ3/f/R1X3UdU9DcPYU1V3V9UdRKT7ok5YawH8W0SWqeq/ASwzDOPfrus+U1ZWtjKar4B6S1XTvu8fUDjnnxaR3QDsWvjYGeH/VgJAXlVXi8hqAO+g6znyNRF5xff9V9Pp9Bu8uNH3Cn/L/gfAF1R1D8Mw9ii85tkTwNBePFQrCq91VHWZiCwTkX+apvm0iKyNIjsBqvoJ3/f3UdW9AOwtIrup6nYAtkPX65GKj/nQeXSd01YA74vIW0EQLBeRFUEQvCEir9u2/Tp/J/uWqqZyudyupmnuXXhe3UtVdy78Df0Eus5r2cd8+E5VbS0spFgNYLmqLheR5SKywvO8l8vKyt7qq6+FKCqqKq7r7g3gABH5kqruIyJ7APgUuhaY9CSHwmuMIAheF5HXVPUJ27ZfYnG777S3t++SSqUOBbCPiOwFYC90vbYYHmK4C+BVAM+LyPMi8nwul/vHkCFDVvV1ThaxB4juN+6qejSAo9D1w9aXOgE8JiJLVHWxbdvP9PHjUwiqWup53okATgBwKLb+d3gZgHtF5G7TNJ/sb38EVHUH13VHicjhAL4O4LMRT7laRB5X1UcBPG5Z1v+JiB/xnLQVVHU7z/OOFZGjVHUsevcGv0ci8paqNqvqEtu2F4nIB335+BROZ2fn3oZhVIrID1R1p618OA/AowDusSzrHhF5pw8iFgVV3cHzvHEADkPXRe790LtC9ce1HMDjAP6CrufO51hIicf69et3Mk3zMBE5CMBBAL6ErlVOcWhT1ScNw3gcwOOF1xmD4gJRXyq8xv9/9s48TJKiWvvvycqIrO5hh1FEkFVQ2RVk32SXRQVRREX2iwsKuAt6RcUFZb0isoOAgOwooI4gCAiugGwCCgiiiKzCdFdGZOX7/VE5fO3QM9PVlRmRmZW/5+F5rtwh3jNdXZkRJ855z1sBbJ6m6RYisimAhQuU7AK4C8DNJG/WWt/SdK1MD5ILJ0myYZqmm2Sf23oAFvUY0jh6xSx3AriD5E1RFN3nMZ7KkT1TNxWRTdA7i60Bd8/UyXgGvc/zLpK/11r/sk77lobqQnIxa+2uALYHsDWmVjTUL/8CcBOAG621VxWRMK0zJBczxuwgIlsB2Ar55xAB4D4AP8vyTb/OI9/UJLEnYIz5GIB9XGiJyKeVUr8cdJ1Op7OSiBwUBMEHSL42j9imyF0ATldKXSAizzvULQxjzCkA1nehpbVer58/T7Jlrd0bwFHoVRUXwQMicloYhueIyLMFaQwMyRnW2t0AfBC9F2I/VWN58zyAS0XkgjAMfyUiqcdYCsMYcxOAGQv8gwNC8pkoirbPYZ2g2+2+neQBJN+OXiu8C2IAl4vI6WEY3li1S6HJGB8fX6HVal3qQktELlVKfbOf/2b27NnLKKW+DGBfFJOMTUTkxwBODcPw51X8TI0x65J8j4hsD2AdlGPv9xx6F6g/CsPw+uYyMF/Gx8dXCMNwd5LvBrAByvGZA8B4VgxxpVLqxyLytO+Aykwcx2uJyN4k9xKR13gO5zcAfqiUurhJaM+fTqezSqvV2p3kbgDegp4NSGkh+aSI3ADgF0qpKxv7tP+GZJAkyUYkdwewK4CVfcc0Be4DcEMQBNe1Wq1ZImJ9B5QXWdHCBS41ReRKpdTXXGqSXMJa+3NHWrOiKPp8TmuFxpidAewvItsGF8EFAAAgAElEQVTB7QVPl+TPAfxAa31V4yIwOdkzbRuS+wB4F6ZWCZ8Xj4vI98Mw/P4g+aaybGpLQRzH3xCRz7nQIrl7FEWXT/e/N8asLyKfJflO+N0cjQO4OE3Tb7Tb7Qc9xjEwxpjrAbzNhZbWesrfPWPM2gDORc/+wAUvkjxVa32ciPzTkeYCMcZsAOAjAHZDztW0eZC1bV4E4Jwoiu71HU+eGGNewNTaiAaC5JNRFE37oE5SW2v3A3AYeu1PPnkIwMlKqVOrPGcgjuM3iIgry5Tvaa0/OpU/mG3ADiP5FUy/Tbdf/kTym1rrH5W9inh8fHzFMAw/SPJ96NnmlJln0Lv8ubAulz8+ILlI9vzbC71qz7Lv8bskf9pqtU5ptVrX1fUSuF9ILmSt3R+9izlX+75+6JKcJSIXKqV+VOX3W56QnJkkyQEk34tyfm5TxYjIz9I0vUhrfeUwezLHcbymiBwkIrvl0OHlk2cAXJa9Y2+q+jvWGLMugD86lj1da32QS0GSr7LWurowvFhrvecgC5BcJEmSjwA4pCTflxdIfl9rfWxmeTb0ZJ1Bh5A8GMUVRU6V2QDOStP0hHa7/XC//3HZN7hOqUISu9PprNZqtY7ObvfL9PklAM5OkuSoqnpfljGJbYzZH8DJmJqXed6MkTxWa32MiLzkQR8AYK3dOE3TL4rIDr5i6BOKyFUkj9Za/953MHlQ9iR25rP2fhH5CoAVCwhtEB4DcJRS6tyyJz4no4xJbJKLGmMu8vhMuDvrpvqZJ/1JISlJkmwD4GOZtZjPLpXpchfJ72itL65T5ViRjI2NLddqtT4uIgfBwXO6IB4RkVPCMPzesNqNZIfLj5E8HPP38S8NIvIPkt9RSp02rJ9bHMeri8gn0OsOdFnN5oKnSX5Pa32yiDzlOxgXkJRut7tDmqaHAdgG5Tpr58E9AI7LOqmN72CmQ5PELoRpJ7Gz2SqHZ++u6fr5F8lskqdorb89LM+xuck+o4+R/BTKt7/oAjhHKfXZfiw5q3jAGUpIKmvtl4Ig+FPWzlS2l2oI4MAwDB+K4/ibJF1VxtWWOI6/AuAM+ElgA8CoiHzRWnu8D3Fr7UbGmFkkb61QAhsAJOuQ+F0cx9daazf2HVCd6XQ6r7fW3igi56F8CWygN7DkTGvt3dbaKv0elxKSS1trfT8T1iT5U2PMmz3G8DIkgziO32utvZvkz0nuiuru79YWkfOstX+11h5Oskjf30rT6XRWM8acH4bhX0XkU6huAhsAViR5jLX2r8aYj5P0te9xDslRa+2R1tpHSH4d5TtgzpOs2u44a+0j1trPDdP3tdPprGytvVxE7gZwIOqXwAaApUTkS9bavxljTiC5uO+AisRau6219k9pml4LYFuU76ydB2sAOMsY86gx5mCSpba6aSgvWQHRfsaYh7KuyDImsAFghoh8ylr7F2PMISSruj/um+wz+h9r7cMkv4ly7i9aAPa31v7ZGLMfySk9d4fmQ6wyxpj1rLW/J3kU/A6OmAojIvJZa+0dxpgNfQdTVay1R4rIF33HAQBBEFzpUi+rsjyF5K3oVUFUFhHZkeSt1torxsfHV/AdT50gGVprPxMEwV0ANvcdzxR4I8nrjDGnD9NBP09ILpn5A67uOxYAjyql7vAZAEmJ43h3a+1dInIRyvFzyYvlSB5rrX3YGHPQMB06FgTJheI4/lYQBH8C8H648/x3wasBnGitfaDT6ezqO5iisdZuZ629h+RXASzpO54BmEnyG9klxP51/r5m37+vB0FwL8l3oZ6JzrlpA/iEtfYBY8yBdft8swuJKzIv3TV8x+OCzGP/FGvt76y1m/iOp6FadDqdVa21vwRwZgnmNUyVhQGcZK29JY7jN/kOpmiy59r1AL4P4FW+45kCS6FX9PWrOI4XaINYq5dQ3SCp4jj+FoDbAazlO54+WRXALXEcf32YKmrywBhzaHagKQP/brVaToZKAEAcx+9MkuReAAejRgcDku9stVr3WWs/01Q9DE4cx2+01t5G8lsARnzH0ycHWGv/ZK3d0ncgVSLz+/0pgDV9xwIAJC/06StpjFnbWnuTiFyKeh+6lwJwqrX2N9lchKElu7R4X5IkfxaRz6D8RQ2DsHwQBFcZYy4gWcbKoYEguaQx5gckf4ZydhBNl5kAzrDW3jqVQ2jVsNZubK29V0Q+D39dkj6ZCeA0a+0NY2Njr/UdTB4YYz4aBME9WQflMLIuyZuNMceRrPM7pSEnjDEHB0FwJ4AtfMcyTTYSkT8aY/bxHUgRkAyMMYdmhQ5b+Y5nGmwqIr+J4/hd8/tDTRK7pGQTaX+aHVSqmvRqicjnrbW3zJ49uwwG/6XHGLMvgON8xzGBS1z4kpJcOPO4vYJkLTbGkzBC8lvW2l91Op0qTDYvJdbaHUTkNvQGl1WVFUheb609cqptU8MMycha+xOU6zO/wIdo1qlyMoA/ANjMRwyeWA/Ar40xZ5KscsXqtCD5amvtz0TkhzV+R07GXtbae6212/oOJC+SJNnJWns/eh7KdWXDLElwaB3ecSQlsze6ET2LsGFnizAM70ySZCffgUyX7NxxMYDvop5WMP0gAA6z1t7anE8a5kVWTHIFgFNQvQKiuYkAnJ1Z4NYmH5rN1rgCwPFwN/S+CBYRkcviOP7avD6f2nxodaLT6axmrb0djoYMOmA9rfVvskEMDfPAGPNW9F4Mpdnwi0jhiZrM0/h2AO8tWqskbBwEwe+stdv5DqRqGGM+QfInABb1HUsOBCS/aq09r+lWmT/W2pNQroTtXVEU3eta1Fq7hbX2LgAfQXUvtwchALBfkiR3WmvL9PtQKNbaLYwxd6Dn0TqMvIrkddbaT/kOZBCyROiRaZpejV5Fa90ZAXB8kiQXk5zhO5jpQrKdJMmPSB6Leln3DMpSaZpeba093Hcg/dLpdFay1v4ewHt8x1Iy1guC4LeNvUjD3Mw5q9etYyGzwL2wDl3SnU5nlewzqosVm4jIEcaYn5B8xbm/SWKXDGvtNkEQ3A7g9b5jyROSywK4OY7jWj388oLkTACXolztiY+EYXhbkQJJkuwUBMFvAdTem2ouFid5bRU3/z4gGRpjTgVwAuqXvHu/tfYGklXwK3NO1p3idCL8gnBxuTeRzFrsGJI3AFjepXYZIbksyRustV+oUwXN3JAMsr/j9RXynCyKFslvG2POr2LLe1YddWlmFVfb39nJILmHtfaW8fHxyj27SC5krf0JyXf7jqWkBCSPNcYcW5WK+ziO39RqtW5Gz/ay4ZUsQXJWc15vmIMx5q1ZbuqNvmMpiPdYa//PdxCDYK3dOgiC36CG+RQR2dFa+7O5E9lDtZEqO0mSvD2rMizrdNdBmSEilxlj9vMdSJkg2bLW/hDAcr5jmQjJHxbp+Wqt/XRWkVTX3/cF0co2/+eSHPZWxnmSDXC8ACVLZObMxtbaWxvbpf8m69452Xccc5Faay90JUbyVdbaX4jIp9Hs2SYSkjzaGHNdHS+AsurPy0kejfpd3A3C+40xl1Wpe2VsbGy5rDpqN9+xeGSdVqv1O2ttFYYwAwBILmatnQVga9+xVIDDrbWnlD2RbYx5i4jcRLLZa82fERG5NI7j5vJmyMme2bMALOE7loL5sLX2C76DmA5JkuxE8hrU+zPaYO5EdnMgKgnW2u3SNL0c5arELYIAwOlxHH/AdyBlwRhzFIBtfMcxCT8samFr7RdJHoPmGQQAe2eVPlX3F8udrBLxPAxHy+cqSqkbSL7adyBlgORi6HWnlO178avR0dG/uxAyxqxrrf0dgMokflwjIttZa3/b6XRW8x1LXpCckb0T3uE7ljIiIjsbY66qwjtzfHx8+TAMb0INq6OmwUySs6rgo5zNYbgSwIa+Y6kQ/2OM+ZrvIOZFp9NZBcBP0RsW3LBgWiJyvrW2jOfTBgdYa7cjeR2ARXzH4gKSX7PWVsrKN0mSndI0vQz1zx8CcyWymwRSCTDGrEuybFYSRRKIyDlxHA9DYmq+GGM2FJHP+Y5jEu6Ioui+IhaO4/gokl8pYu0Ks7Ux5vIqVZe5wFr7bQB7+o7DIatZa6/P7IWGGmvt8QBW8h3HJDixErHWbg3gZjRDxKbC8kEQ3GyMeYvvQAYlq/78OZrqz/kiItsbYy4ps4/l+Pj4iq1W60YAK/qOpUToNE0vLfNMkMy7/EwAW/iOpWqIyBeMMR/zHcfckFwyCIJr0CSw+yUieUUz02r46HQ6u5C8GtUeDtgvQvIskpVI2g9ZAnsOG1hrryPZbpLYniG5tIj8BMDCvmNxTEtEzu90OnUxn++bzELibJSwVbgoz9c4jr8mIl8qYu2qIyI7JElySRX9PovAGHMwgGH0DF/dWjtrsiEWw0KSJG8HsI/vOCYhVkpdVrRIdnj4CYDKDkPzwEwAv6xaFc1ESC5prb0ewMa+Y6kCIrJTdtlVOjqdzspZAnsFz6GUkTbJK621W/kOZDKSJDkSwPt9x1FhjrPWluYZllnSXY7GA3u6LATgkqw7rmEIsNZuHATBjzBcydE5LG+t/Y7vIBaEtXbTIUxgz2Eja+2pTRLbI9mL9eIh9uZSQRCcH8fxULZZGmO+AuANvuOYhNRae1Hei1prPyUiR+S9bp0guUuSJBeSDH3H4hNjzAboDXEcVtZOkuQHdR5aNy9ILkbyNN9xTIaIXCsizxWpEcfxbkEQXAag8cnvn4VJXlvFgVSZfcEVAN7sO5aKcUjZKj9JzgyCYBaaLor5MULyx9baTX0HMhFjzIYkm0KLwVAALi5LR1mSJJ9DY8k1KCsnSXJO2T3PGwZnfHx8RZJXYLj3oPvHcbyW7yDmxdjY2HJD5uAwGXsP3QG5TBhjvozmxbqwiFwxbFWHmY1IWatMbxwdHX0izwWzoQPfzHPNukJyt8xGYyjJ2rguxnC/nEFy16wibKiw1h5H8rW+45iMNE0LmxMAANbat4nID9FLAjRMj0hELs7sWCpBZl9wOoDNfMdSUY7PLj69Q1Jbay9DYyEyFWaQvLLT6azsOxAAILkQgPMADHURQR6QXDZJklN9x2GMWa+5lMgHku+w1h7oO46G4iC5aKvV+gmA2g3L7pMAwNd9BzEZJEfCMLwCwNDPT2qS2P54q4h81ncQJWHVJEnOG7Kqw7NQQhuRjFwTNXEcr54lf8r69y0jhxpj9vUdhA+y9vDlfcdRBkj+b5IkO/uOwyFvA1DW3/sXtNY/KWrxbDbGFRjyy5uc0CQvN8as4zuQqZAkyREAPug7jgoTAjg/S0J6xVr7PTSXEf2wZBAEV5H0bqlorf0KgFV8x1EXSL4rjmNvw2kzv/yz0VwK58k3SS7tO4iG/BGR0BjzIzRDiAG8bFe2ie845iYreKj8/Jc8GKakYakQkU+iue1/mcxGoYwDDovijb4DmAedPD1fSS4pIldjSCYb58zJcRyv4TsIl4jITAD7+Y6jRARpmp43Pj6+gu9AHFFGe6U5XC4inSIWJvkaktegeU7mySIkry37dyeO4z2aQce5sIq19kSfARhjDgWwv88YKsrq1tqTfQaQPSc+4jOGOiIi/+frgsJauzeAodpDO2Dxss4haBgMkruKSGkH7vqA5CG+Y5iIMWZvNPMaXqZJYvujSWDPBckvxXFc5iRG7ck8X5/Pa70kSc4GsFJe6w0ZIyLyw2wA6LDQVOu/ksWCIDjFdxDDTlHDbkkqa+3FIvKaItYfZkTkNa1W6zqSS/qOZTLGxsZeKyKnAWh8RvNhP2vt9j6EjTFvBVD6YVAl5oNxHHs7nLdara+i6YIpguWSJDnMtSjJEQBHudYdEvYsi31TQ640HQuv5F0kS2Gtks0YONZ3HGWiSaQ2lIlIRE4luaWI0Hcww0iaprklauI43ovkLnmtN6SsmXnnD1OXQsNciMgOcRy/L4qiC33HMoyIyD/CMLyxiLWttd9CYz9QJG8wxpxHcqey7StardbpABbzHUedIHkiybVExDjU1NbaM9Fcwg6EiHyX5A0i8k+Xupkn914uNRdACuAJAA8D+AeAFwEYkrODIBgH0EnTdIaILARghogsQXIF9Do8Z/gKel6QPJzkd0XkWVea1tp9ACznSm8KxAAeJfmwiPwbQAfAbJImCIL/AOimabqYiMwAsFD2ma6Gnr1NGXM1XwXQVO021B2dJMk+AI7xHYi19jgAS/mOo0yU8cHYMNxsbq3dH8AZvgMZQp7XWl+bx0IkX+W7tXcBGPQOCePZPwuJyIxsoFypquJE5PA4jn8YRdGffMfS4A8ROZ7kT0XkOd+xDBskLxKRbt7rWmt3IHlo3us2/DcisqO19lAApWmDNsbsD2BH33HUkNWSJPkEAGfDkY0xR4hImW0LngLwEoDn0Dv3LYze4C7vHuJzsVhmVbCnS1ERORB+O5MfBjCL5C9I3h1F0aMiEve7CEmJ43hFEVlbRN4BYFcAi+cebf8saoz5FIAvONT8H4dac9MF8AeSs4IguDFJkgfb7fbfRSTtdyGS2hjzBhF5K8ndRWRrlKNidltr7eZKqV/5DqShEhC998+zAF4AsCh6F/iLoeS5SJK7wXMS21q7HckP+IyhD/4D4Gn09hwWwOIkR0RkaeScXylVssY3cRx/Q0SqXPFoAfxVRO4n+W+S/wHQFZEIvc3q8iLyRpLL+g1zgTynlHqDiDzlUtQYcz16g8WGlTO11gfksZAx5mIA78ljrZz4K4BrSN6Ypumd7Xb7scmSUiTb1trVAGwiIluR3AnAiPNoX8mvlVKb+qokNMa8gGr79T4H4IHsn9kkXwQAEVkEvb/Xauj5MZftUD83p2utD3IpGMfxG0TkfpeaJWQ9rfUf8lyQ5KJJktxTgfdxXTAANtJa/9F3IGNjY8uFYXg3ege5MvIUgPsA/IXkE0EQjJN8QUQWStN0cRFZQkRmknwDes/Osh1CX1RKreJiD2mMWRvA71COxBLQq9idJSLXA7hFKfWgiLww2R+cPXv2MlEUrdPtdrcUkZ1QkoFeIrK9UurnLrSyKvrH0UvqO4PkP4MgOLXb7Z7fbrf/WpCG6na726ZpehiAbYrQ6IPnlVLLisjsooWMMRsCuK1onUn4I4DvKqWuKqrqnOTi1to9AHwKwOuL0JgqIvJjpdSuPrSNMeui9/N2ifP9d1YQ9i+XmjnxewC3krwtTdPbR0ZG/jbZH8rO3OsAWB+9S7e3oXxWx6lSaumsg8I5JFvW2vsArOpDfwFYALeKyCwAt4ZheN+8fk4kR7L8yloAtiG5bZbYnjZNEnsCFU1iP0TykiAIfhGG4a+ncntPcqYxZjsR2RnAOwGU0XP3OK31J10KDnsSW0S2VkrdMOg6SZLsnKbpj/OIaUBSAJeIyMlhGN4ynQQwyUWtte8D8BkAK+YeYX+xvDuKotyGbvZDBZPYXZLXici1aZr+ot1uP7Sg/4CkWGvXEZEdSO4BYF0HcfZLN03TN7Xb7QddCTZJbPxZa537IF5jzFkA9s173Zz5M4A7ST4I4CEALwZBMBu9jWs7TdNFMi/vOZdAGwIY9RbtgnlQKfUWEXnJZxDGmIsAvNdnDHORArgRwMVpmt7UbrcfmOp/SHKhOI7fFgTBbgB2R0kuAkkeE0XRZwvWEGvtb9A7gPvmMQDHKaXOF5FnprNA5ut9CID3wa81yp+UUutOp3K1XzzsVxOSR2utjxGRMVei1tptSJ6Cnj2FLw7UWhfeZWuM+S6AjxatM4F/kfy41voSV4UmJENr7ccAfA3+LGS63W535XklKIukSWKXkn8DOI/kGVEUTevMMDY2tlyr1fqoiHwCJcpNkfxgFEXn+9CO4/j9IuJFez48LiLfD8PwLBF5cjoLkAy63e72JA8iuSumcXnRJLEnUKUktoj8GMAJYRj+cpCXJsmlkiQ5gOSnAJRp+NFspdRKLquxhzmJLSJPhGH4ukEPDSQDa+0d6N20eYPkzwEcHkXRvTmtp6y1BwL4OvxVzz2klFpdRKxr4QolsZ8neWK32z19dHT0iUEWMsZsKCKfJLk7yvWuPFdrvY8rsWFPYovIF5VSX8tzTWvtFiRvzHPNnEgB3ADggiRJZvX7HSIZJUmyUbYh/SDK6d93qtb6YF/ixph1APwB5ag2GgdwarfbPX5kZOSxQRcjuUiSJB/N9pNLDB7eQLyklFphugndqRDH8e4icmlR60+R/wA4Uil1al4+4J1OZzUROUlEfHre7qu1PqdoEWPMSegl7l3woojsoZT6mSO9/4LkYsaYiz1+rndord9ctIgx5kG4q1K+p9vt7uwjkQu83AlyNYDX+dAn+Y0oilzaxABoktgl40UR+WoYhifm9Q4aHx9fMQiC/8s6hMqA888eeDmncg968w7KwLMi8vUwDE8WkU5ei8ZxvCaAb/T7eZdhE93QH78RkU2VUrsqpW4Y9NZXRJ5WSn1TKfV6AMeh5+VVBmYYYw73HUTJ6QD4HclrAZwP4AwRuRrAbST7GoyTpumFeVS9GGPeDb8J7NkADtBa75BXAhsARMRqrb+XJMnqAK7Pa90+eb219v2etMtOQvI7SqmVoyj68qAJbADQWt+ulNpDRDZFrzWuLLw/G0TVMA+y59+NInIFgDPRqw75OYC7AfRT/cZut5vrME2SAcnj8lwzBzoATux2u6torbfVWp8zne+QiMRKqRu11ocrpZYluRd6thRl4kBjTOGJlHlB8qsox977AqXUSlrrw/JIYAOAiPxHKfUNpdRqAM7LY80BWMgYU5jfPMlARI4qav0p8oskSdbQWv9fnoMs2+32A1EUbQ9gP/T3vMyTI0m6sKnZ1oEGAIDkvr4S2AAgIs9rrXcCcKqnENbNLvEKY3x8fAW4S2A/2+12d/KVwAYArfVdSqkN0LsYdU4QBB8iWYb3WYN7COAHSqnVlFLfzvMdNDIy8ojWeheSR2U6vlnbh6gxZg+UJIFN8jql1OpKqWPzTGADQBRFd0dRtHPmPz5l25bmwVMdrIgcqZTaRCl1a96Li8hzWutPisiWALy9kCciIh8hWabq8DLwL5JHZxcZi2mt3xpF0U5a6w9qrQ9USr1Da71xFEXLpGn6egAfFpHL0EtSzBMR+eGggZFsiciXB11nAB4DsKnW+syiWvpGR0efUErtgN6Fjw8+12wYX8GDADaJoujTRfgQKqV+rZTaiOTX0atU9U0YBMHnfQdRQm4GsF+apqtEUbSM1norpdRuWusDtNZ7R1G0vdZ6LaXUEpnf/dEA7lzAmrfn7Vdqrf0QAG9J1Ek4N0mSVbXWh46MjDyS16IiEkdRdKFSai0A+wAoSzVRAOAEks67K6y1G2c2bj55Jk3TXbXWH5huG+iCEJGntdZ7o2eX0/eAuhzj+BjJQuxtjDF7Ali9iLWnyPFKqR1GR0cfL0pAa302gE3g50ywsjGmUMsdkkujZ4NUOCJypS87uLniSJRSHxGRK33oZ51thdFqtbYscv25+EJeF4CDICJPKqV2Qu8M5BSSyyRJsrFr3QbvjJHcS2v9IRHpq2huqogIoyj6MoAD4D+RvQZJpxZbJEVEjnCpOQ9SEfms1nqnovaMc4ii6IrszDClgbFNMqQaPC0iWymljp5sGF2eKKVuUUqtiyn+AhXMwkmSuPQ1Ky1ZZeGHlFLLR1F0pFLq1gX5n7fb7b9orb+vlHq3Uup1InKEiPx9kj96n9b6jkFjtNbuBX83hg8lSbKx1npBSamBEZEku/DxYT20mjHmnR50S0k2WObNWuvfFqyTRFF0RBAEO6M3cdk3e8+ePXsZ30GUgcxaax2t9eZa67MXlHSeUy0cRdGRWut1RWQTABeh5/M8NwNf7k2EZFtEcrUmmS4i8kQQBG/XWu9TZCJMRLpa63OVUmsA+FFROn2yWdEJsskg6fuzvz9N0w3a7bYTD2Ct9TlZ0r7wYW7zYDFrbe4DpkmGIvK/ea87VbID5eFFnwcAQGt9Z5Ikm6Hnie8UEflMketnnXVOEJHTXGktCBFJwzD8IHqdSa61313k+iRdDSedrZS6wJHWAhGRf6E348p55wTJQj/ThtLxN/QKhy5yIaa1Psvn+zZj1BjjdJBqkiSbAljTpeYkxCT3Ukod48rvP7uU2w7AArtgmyR2+flrmqYbF1F9PS9E5Dml1PZZK7ZXSO7jo2KqZPxAa7261voHUxncORki8m+l1NfDMFwRwD4i8o8J/7+8EjW+/OQfTZJk6zwsJPpBKfUtH7ekItJc7PQ4OQzDd7mYdj+HMAyvA7A1gKddac4DpbX+gOcYfPMMyQ8opXbVWt813UWUUr/WWr8vSZIVAZyB/19tnyilck26WmsPJFmGy4cbwzBcO/t9dkJWnfteAAdj8gsDp4jIMUVV6U5GHMdrANjKld4k3KmU2iTvzoIFoZT6RZqm74M/q7rcfSyNMbsBWDXvdaeCiHxJKXWMS83R0dHHlVJbwH1F9lrW2s2LWpykkypsAGi1Wl7sHuZFNtx2H7jrLiOAh0XkfpJFzpVxVUjzgO8BwXOjtb5DRL7qUNICuItkrtYCDaXmEaXURi4KxiailPpqZp3qjVartZJLPZJ7u9SbhC7JPaMouti1cFZs9H4AZ8/vzzVJ7HLzeLfb3brdbvuogOiEYfheks4OufNgxSRJCtvElpwUwCFZu85zeSyYVRGfG4bhapk9wniSJAMnsa21GwNwVQExkdkA3llkJeH8UEp9HcA5jmW3iuPY2eGrpHxfKXWIi0q0ucmqvncA8KJr7YmQ/JBPfc/8NU3TDaIoyq0SanR09Amt9YEA1gNwM8lZeQ4WJhmJyGfzWm8AzlRKbVfkwLv5obU+VUR2BPC8D/0JLGetdTaoR0QOdKU1CX/OChNy2Uf0S7vd/rGIfMWHNoCN4jjOdU6HiByQ53p9cIFSymWS6mVE5J8kd4Hj9x7JIoewrlbg2hNJ4f/i+xVorf8IoIhq4nH05oicDuAQEUxNw2EAACAASURBVNk8sz9cWSm1m4i8UIAmAEBEnHymJHPbG+RJGIYnohhbkWfQmwd0HHqXH+sqpRbSWq8TRZGvAqYGtzydpumORdmHLIg0TT8Kf11dSNN0WVdaJEcA7OFKbx4cHEWRF9spoGcno5Q6MLPEnZQmiV1enknTdFufAyOyYXZ7ALjdVwxArxrbp75HPqW1/m4RC4vIS1EUHaGUWjEPH1SS++cR1zR0Dx6kCjMPlFIfBvBnh5ICwMvPuyRcrJT6qKvWpsnQWv9BRN4FILdBJtPgTcaY9T3qe4HkP7vd7jZFVZRqre/IrEn2zXNda+1+JF+b55rT4ORsU+i1ElopdT2A7QH8x2ccAD7hYngcyTYAX50TL5F8V54XMtMhDMOvAyjU9mleiEhuF37Z4Lit81qvD+5RSvm8CEEURXcDOMSx7O4klyhobVczdwIAMx1p9UWSJEdggM4Ykv8k+VOS3yL5PpJvUkotrLVeX2t9kNb6u0qpm0XE1bPeyWcqIq92odMvIjIOYBDrhRTAgyJyiYgcEQTBzkmSLKe1XkprvY3W+pNa63O11nfmOcivofQYEdm13W4/4CuAkZGRR0ke60uf5HKutIwx7wBQZMfKgvi+1voMj/oAenaEYRh+AMCkeZ4miV1O0iAI9vb5sJiDiMxOkmQP9G5hfbE7yRke9Z0jIldprY93oDPwsC2SCwPI3XdyCrrXRFF0vmvduRGRjojsB4ft0kEQ7DmkAx7vVUrtLyLeBywqpa4XkS94DmPYqrEZBMG+IyMjjxYtlMezcQ6ZJdYn8lpvmpyRdS/4HpADoNfRICJvh8fKGgArGGMKHTQGAJlGUYm4+ULyw1EUubxknRQRSUTkUPgZ0LR7XrZ0QRDsC/dnpxTAAVmCyita63Mdt3Vra20hnrsisnAR606GMWZbV1r9MDo6+jjJn0/hjyYA7gFwgYh8RkS2U0q9OoqiZaIo2jGKos9FUXRRFEX3++iQm8BCjnTWJPkaR1p9kdmgTaVj4iUAtwH4Pno2XxsppRbRWq+mlHpPZkF5zejo6GSzlBqGCJLfVkrd5jsOrfX34GlYtIg4S2ID+KBDrbn5k1LqMI/6/4WIdNI0fQ8meaYNYxKk9JD8VhiGXr1/JjI6Ovr3rNLW1+F3YWPMrp60ffBcGIYf9h3EVLHWvhfuNo5ziLPWolKQvdxzHQI3P0guO4Q2O7NJvtulB/aCCMPwOJI/9RjCe11PzPbM2Uqpn/kOol+SJNkW7lrXJ+N6pdSHy5LAnoNS6laS+8Lj5HkR+aQDDV8VtL8ow0XvHJRSt5G8xoP08tba9QZdhGQgIvvkEE+/nK61/o0H3UkheRh6SU1XvK+IRUk627eKyOEklSu9PpnbUuR5ADcBOAm9rr+3ZNYRa2qtP6CU+rZSKle7rTwgGQHQjuRCa23h747pICJjAOaeafUYyZ+QPJrkHmmarqqUWlRrvbHW+sNa61O11reXaX/dUBr+orX2PZQawMvFJc49mgGA5NKOdGaIiI9uL6C3Fz9QRErlc99utx8E8Ao7xiaJXT4e0lof5TuIuYmi6CoAl/jSF5HtfWl74NO+PKemiY8bwzN9Wu1MRpqmX4HDgWXDNhFcRL5WhorCiYgIu93uQeh5QPpgKWvtWzxpu+YppVQpD41T4GMetf+mlNpTRFwmnaZMFEWX+GwRBbB+wcPjlgKwWVHrz4cUwKc86M6XIAiO86Gbx/sySZItALwuh3D6IU6S5GjHmvMlO1C6vBzZnGQR9g0uL4DXtdZ+x6HelNFaXyUiX0rT9B3dbndFrfXiWusttdaf0FqfpbX+43SHyjvG9YX+YUmS7OxYc6qcCOAwEXmbUmpJrfXyURTtEkXRkVEUXdputx8qQ0djQ/kJguDjZUpq+hrwKCJO7D2SJNkMQORCaxIuyOY+lQ6l1GkA7p7475okdskIguCwsm4Wut3u5+CvjWMbH7oe+JtS6lzfQUwVkosB2NixbJIkyTcday6Qdrv9FxFxOQShrJvnIngoDMPC7XWmQ9aO6yUxAwAiUso25bwRkZNExPcwwL6ZPXv2MiTf7kmeIvIBESndULGJaK0/D+AOX/ok9ytqbWPM9vCw1xaRy3zPi5iMMAxvBPCwIzkD4E4A54nIPYMulqapj+/xub4GV88Pki4TsoG1NvefPUnXw5k/bow5PRvaVRpEZEwp9dV2u321C6uuAhmHQ1s/AEGappcZY3Kdn5EHWus/aq1PUEr9UkSe9R1PQ2W5v9Vq+ew2fQVJktzsSdpJEpukr3yX7Xa7R3jSXiAi0p27c7JJYpeLG8Mw9NFqOSWyAYCn+NAm+do4jt/kQ9sxx5e1Ym4yjDHbACh8MNZESP68jIe6jNMdai0fx/FaDvW8QfKIsl7uAYDW+lsAvCQKPW54XPJSGIbf8x3EdNBa7wn3FWJzOFMpdYsn7SmTvfMORq962AfvIFlIG7qI7FjEulOgkKHQgyIiJJl7O7CI/IPkz0geQ/IDJNfKLBDW1VrvrbU+LweNHfKItU/O9KC5QKIouhfAr13picguBazpOokNAAdYa+8zxnwos79oyInMLsv1Z6oBnGWMud5au6Vj7YaGojmlbDZ0M2bM+AeAQga7zw8RWcSRlK8z3WUjIyOPedKeEkqpWQD+OOd/N0nsEhEEQSlbzSbS7XZPhNub7pcZgmrsZ5VS3qfB9oOnQ11pK9XDMLxeRJ5wpRcEwTBU4T6stb7cdxDzQ0ReJOnru7vxEAy+PV1EnvMdxHQguZcn6WeUUq/wkCsrWQvjaZ7kF+t2u7k/SzO/eh9WaPcopX7lQXdKBEHwywH+8w6APwA4G712+W2UUjOVUq+NomiHKIo+G0XRBVEU3S0iudl7jY2NLQdgjbzWmyL3lbW1N8PZXozk2woYZu0jiQ0AKwA4xxjzaBzHxxhjhsUSzAW+PtO3kfylMeaP1trDx8bGlvUUR0NDXowppX7gO4jJEJE7XWuSLLwSm+SrAHgpThORk3zoToMT5vwfTRK7PNzXarVKM8xxXoyMjDwqIj/xoT0ESeyLqzRUg6R4SGJbrfV1jjWnjIikjv26XFu5+OAEz9Pup0SapqfA7bCrOehut1vrIZ8kz/Idw3TodDqrAvCSoBCRE6rWRpx5/xof2mma5j5jwFq7PoCl8l53QYiIl8FHUyUMw9sxtWflYySvIfkNknuSfJNSamGt9Xpa6/2ydvnrXdjlhGHo/MKe5I9da/ZDkiTXwt1Q1kWNMavnuaCIeO3oE5GlReTTAH5vjPlLHMdHD0t3XYH47tJcl+SxYRj+zRhzszHmEJKv8RxTQ8N0+LWIvOA7iMkg6aNieEYBF6n/hTFmMwBSpMY8+LNS6jYPun2jlLoYwDNAk8QuDSJyTtlaNuaFiLi0THiZNE3X96HrChG5zHcM/WCMWYPkax3L3uapBXTKkHSWZE/TdCNXWp4wSimXA6SmzcjIyGMkf+ZDm+R6PnQd8UAURQP72fogCIKdPEm/EIZhKe0k5sfo6Ojf4a/TJndLERHZKs/1pkqaplf70J0q2Tt8YiXVGIDfomfHdYiIbKmUWiIbRrZzFEVfiKLo4iiK7vdlt+ZjuHgQBD93rdkP2ff17gX+wZwQkVwv7dM0vTfP9QZkZRH5gojcZYx5wBhzYpIkOw1Bl1XelOUzDQBsCuAka+3jxphbrLVfNMZskHXoNDSUGhEpsxWdj8sqQfH2qV4uMUmWutt5IiJiAFwFNEnsssAkSS7xHcRUabVavwDwH9e6IrJ01mpRR54Ow/Am30H0g4hs6FqTpK+BDlNGa+3SJ/I14+PjK7rScw3JX1TJRkJEfG0Ealu9RbJSl3tz4csP+YwqDsEEgDRNj/UkvXiSJHlfCr455/Wmwt+iKPqTB92+CILgyyT3SNN01ay6egOt9UFa6+8qpW4q4XPf9X4nCcPQ2V5iAFzGmPf3876c18uLVQF8PE3Tn1hrnzXG3GCt/Zwx5s1FVwLWgLIksSfSArAJya8AuN1a+5S19kfGmAPGx8df5zu4hoZ5UNoktqdKbABQRS4eBIFryzIAgIhc4UN3ugRBcBnQJLHLwu+qNBE6G7DmZQBlkiR1TdZcWaWBjhnreNAs/eFcRP4lIn93pRcEQW2rsUXkR75j6Ael1NXwMDOAZF2fi5XrUJlDVkHnxeaFZCl9DKdCu91+AMDtnuRzrfQk6eMdWfqLXgAIw/CaKIoubbfbD4mIr4GeU4Lkqzx0nT0oIh3HmtPhDw61cv1+aq3/gl4XQJnRALYi+Q0Af7DWPmmM+aEx5sNxHK9O0kfreWkREWedAQOwBMk9AJzearX+Zoz5szHmu3Ec7zl79uxlfAfX0ADAZrZfpcSjFVShldgk1yxy/XnwjFLK5Xt8YLJi2tlNErsEkCx1y+BkkPSSxEZNKw6r+DsAP0nsKmxQkabpXa608m6xLRPW2lm+Y+iHzJvVx8ZvlZq2Hf9bKXWH7yCmQ5IkGwOIPEjfVYVK3AXgxUIoTdPcnqXZEKCV81qvD37jQbPWJEnS7HXmjcsBW6uQnJnXYiKSiMj1ea3niJkA3gfgeyJyj7X239baq6y1n8ysKopudy81YRjeCuAl33H0yWoAPioiFyqlnjDG/MUYc44xZr9srkZDg2seK/OMrm63+5Qn6cIqsUmOwsOeUUR+XRU74zlkliK/bZLYJSAIgtJOkZ8XaZre6kO3rhWH3W63Cm2jL5O1NDr/LLTWf3OtOR1E5GGHchs41HLJX2bMmPEP30H0C0kf3+XAWuulDa1IROS2qm2uJuClQ6Jq3QuToZS6BO4Gxr2MiGyYV2VjkiRrw8+AntJWT1UY50lsj+3SfaGUesShnHS73bfmuSBJL4Pqc2RJkruS/A56VhXPG2NmWWv/11q7VZYYGRpEJBaRShU/TMLKAD4E4MwgCB6I4/ifmf3IIcaYdRpLmQYHPOo7gPnRbredW9pmFHZJaK1dHX4cMrzk8waF5C3Ng9A/NgzDSkwEncjIyMijJP/pQbqOiZq/j46OPuE7jn6I43gVAAs5ln1BRMre+gkAEBGXyfa6VmpUyiN+Ar4upFb3pFsklU3I+Rq6SvIXPnTzRESegh/rqKXiOM7leUpy3TzW6ZNUKVVGT9hK48MWRkQqcYErIs8AcDZsm+Rqea6XJMk18HBhViAzAGxD8sskb7DWPmeMuTWO428kSfJ2kov4DrBoanAx8V9k86D2AHASgDustc/Ecfxja+1njDEbkizUp7dhKCl7wZizd85ExsfHC0tik/R1lv+9J92BaLVatzVJbP88JCJVa30CAARB4CPBsKwHzUIp2YT0KSEia3uQ9dU+1Ddpmrq8lFiE5Ksd6rmikjYSWmsviVcRqd2zsdvtVu7ZOAcRybVicIo8XzVvu/ngpc0/CIK8Bvg5P5CIyD9EZNy17hDgvOuMZGX2OwCcJdxJvj7P9UZHR5+oqJ3fVNEANhaRz6Vpek02KPK3cRx/zVq7WR3tR5RSlwHwVanpgsVEZGeS3wJwm7X2uTiOrzPGHBrH8Rt9B9dQfUTkUd8xzA8RsQBqtdcJguA1PnSttQ/40B0Ua+29TRLbMyJyv+8Ypkuapj4me8+s261zFX8HgiBYwYNsFYYcAQBardYLLvWSJMn1YFcGqvi9AAAReRLAs651SdZxINCffQcwHUguDWAJ17oi8isRcT5YtAjSNPXSiZFXkszHpRLJv7jWHBJW8KBZmf0OgOcdaq2S94JBEByX95olpgVgfRE5guSvrLXPWGsvN8YcWJdiCBF5AcCZvuNwyAwR2QHA8SJynzHmb8aY0+I4fgfJEd/BNVQPkmWvxAZqdlFF0kcSe/bIyEilnADm0G63H2+S2J5J07SSiRoAEBEftzfB+Ph4LTZaE3jQdwD9QvK1HmSNB81pQdJpEptk7gc73xhjKpnAzHD+nRaRuiWxbRRFLr3lcyNJkjf40E3T1OWQtUIh6Ww47kREZMU81vH0jqzk96XMkFwMPYsG17rWteZ0cbzfyf3CPgzDWQDuyXvdirAIyXcBOM1a+w9jzK+MMYeNj4+v4DuwQeh2uycBSHzH4YnXAThQRK7MBn9eEsfxXtmw4YaGBSIij/uOYQrUKokNwEcS+6Gqzh0Skfq1EVUNEaly5YyX5GsYhssA+LsP7SJwbD2RCyLyWtL5c29Fa20lhpZlB1+XenVLYs+u4lDHCTwIIC9bgilRt0psEXlKRCp5CCXpJYmNGiVi2u32Y9baFwC4PnjnksSGB+uzillQVAJjzLIi7udzBkHwaWvtB50LT4/VHe4HlyMZiUic14IiwjiOvyQil+e1ZkUJAGwGYLNWq3WcMeZ2AD9QSl0kIs95jq0vRkZGHjXGnAXgIN+xeGYGyXeLyLuttbG19pput3tuFEXXZZYMDQ2vgKTL7prpUkkr3vng/AznabZdbjRJbM+IyL98xzBdlFKPWOv+HVi3ZE0QBE/6jqFfPFWZLZENN2mYCxGpm51IZZ+LAEDyEdeJj7o9F0lW7rk4B5LL+0h8oUZJbBGhMeZeABu71CU5cBKbpLbWzswjnn4IguBp15p1JwgCHxf2ALCZJ92yExhjVgKQaxdrFEVXxHF8jYjslOe6FWdDABtaa4+31v5YRM5qtVo/E5HUd2BTQSn1eWvtuwA4fxaXlIjkbkEQ7Gat/bcx5kKSp0VRVNnZIw3F0O12q5DErtsljPNK7GyIemVp7EQ8U+WDOoBnADjfzIjI0q41i6Tb7Vbxd6B2Q+Qqzkq+A8iZSiexfWwMRORVJGvzTq/yu9HTO4pa67rZSTziWlBEXj2oj2in01kGHvbXJJskds54urBvmA+tVquQ/U6apocAGCti7YoTkXx3mqbXWmvvN8Z8jOTCvoNaECLyLIBP+46jpMwE8HERudsYM6vT6exSp/1jw2C0222nlpjTpBbzXybwKteCJP/tWjNPmgeWZ5RSlU3WZAOknvGg69yfsEDYbrcr9TuQbXRqdZFQA5byHUCeVLlDBfC2MWgBaHvQLQQRqWybWzbY0TXP5tliXwY8tTpKHMcDJS5brZaXyr80TZ0PlK07ItIksUtGmqaF7HdGRkYeAfCJItauEasC+D9r7eNxHH+d5JK+A5ofSqkfiMilvuMoMQJgmyAIrrbWPmiM2Y9srGaHHKIaftOVtBucD86HsAZB4DyHlydNEts/L/oOYEB8JGvqNG35uQomHmYAUL6DaPgvlvAdQJ6QrMIGap4EQeDrdrs2SeyKV2I7r6gAUNmk/7zwZbUVBMFAVYYi4mWP0mq1qraXKD2u51s0TInC9jta6zMAfK+o9WvEoiLyeWvtw3Ecf4Xk4r4DmgwRYRiG+wL4k+9YKsDKAM7Mqu33JtnyHVCDF16qyDyaKsQ4JUgKAO1Bt9J7xiaJ7Z9K/wIBcN5ykqZpbRI1AKqYqKnTJUJdWJhknS4WKv1cJOmlFW9sbKw2z0YRqeKzcQ4+npFV/nlNSpqmXv5OIjI64BK+vofGk26dqc0ztS6QLPTSXil1KICbitSoEYuIyBezKt79s2RMqRCRl7rd7jsBNHZLU2MVAOdaa39vrd3IdzANbhGRKliJADVKYgOI0OuKcE2lz9pNEtsvaQ2mAzv/AohInQ4VlUs8dDqdOv3864IAKGUlzDSp9IvV1+12q9WqzQVTlSux4SHxJSJ1m9QO+POnHciyLE1TX9/DJomdP81+p2SISKEWFiJilVK7APhVkTo1YykAZ1hrb4njeE3fwczNyMjIIyTfhorPW3HMOiRvMcacXtZK+4b8IVkJhwCSdfLEbgofpkGTxPZLpRM1gLdkTW0SNQAqNxm2ZpcItcEYU2pvwj6p9IvVVxK7Tt/NIAgq92ycA0nnn0PV2wIno9VqdXzopmk66NwNL99DkpV+bpaUOu0360Lh9mki8qJSakcAs4rWqhkbi8hvjTEH+w5kbqIoupvkliLyhO9YKkQA4ABr7R+NMRv4DqbBCVWpcK5KnFPB19mt0j/DJontl8r//IMg8HFwrk2iBhX0RK9ToqxOBEFQJ1/sSj8b0zRtktgDQrKylcUi4uP310vCt0hIjnuSHiiJ7fF72HiY5kydnqk1wsleR0TGsors813o1Yg2gFOMMReRLNUlUBRFf+52u5uj8cjulxUA3GyM+bjvQBoKpxKJzSAIalOJ7bHDvdI2pJVOFNQAXUb/sD6pzUPEE7N9B9AvvoZWNcyfbrdbpyS28wEXeTIyMuLruVj198nLpGla2SQ2/HQS1LEK14vdmogM+vzxciARkUofSMpIzWaw1AVnex0RibXWHwRwMGp4UVgw77XWXktyId+BTKTdbj+slNoQwNm+Y6kYCsCJcRx/1XcgDYVSFZtb+g6gBlT6rN0ksf0iqPgtSJqmPr4AtdlIkqxiErvSD726UrPLhch3AIPQ6XS8fEdI1ubZ2O12K/dsnICPhHLoQbNofH2PBvXi9lXJ1Lybc6bZ75SSQQev9o3W+lQAmwJ4wLV2xdnSWjuL5MK+A5mIiIxrrfcDsD+A//iOp0qIyJHGmO/4jqOhMCpRiV0n2u22LzvASu9vmiS2fyr9C+Rpg1+bRE0QBJWrNmx8N0tLnVrJm+fiNKhTEnt0dLRJYvdH7SpGfVk5DHq57PF7WOmiiDJCsipVacOEl7Or1voPSqm1ROQzqKAVoEc2TJLkfJKlyzlorc9SSq0G4Bw0lZ398EljzEd9B9FQCE0S2z2NBeU0KN0LZQgZdICQb5wna+qUqKliJTZqdIlQM2rzPBeRSj8XfSWx0zT15SFcBFV8Ns7BeRJbRCrdvTAZ3W7Xywa71WoNWont5XvYWF/kTxAEzX6nfHi7sBcRo5T6dpb4PAHVfk85g+SuxpijfccxGSLypNZ6XwAbiMhVaJLZU+UEa+3WvoNoyJ0mie0eL0nsNE0rbUNam6RHVTHGzPQdw4A4PzjX7FBRuQ0wSV9tLw3zQURqU4lNcinfMQyCr4TiyMhIXZ6NHRGp8kb6edeCaZo6b7F3gJfLrEEvl1utlpfvoYhU+kBSRupUNFEjvJ9dReSfWuvDlFLLi8iXATztO6ayIyKftdZu7juOeaG1/p1S6p0k1wBwLuo5ZyJPQgA/ILmo70Aa8oNklffeVcXXnrHSOUjvG4FhJwiCSv8CAVjcg2ZtDhUkK2cnkqZpbX7+NaNOz/NKPxdFxMdzEajPs7Fyl3sTEZFnPGi+yrVm0QRB8GofujnYiXipxG6S2IXQXNqXj9Jc2IvIM0qpo5RSy5LcC8D1AFLfcZUUIXk6yVLPb4mi6D6t9T5KqeVE5NMA/uw7prJCchlr7bd9x9GQK00S2zEi0oWHvUbVC8bqlPSoJGmaVjpZA2BJ14K+DohF0Gq1Kpes8TiAoGH+lOZglwOVfi6maer8uYheC2yTxC4BJJ1X5ZF8jWvNovH1d0rT9IUBl/DyPax6a2hJqcsztU6U7uwqInEURRdqrbdJ0/T1JL8G4GHfcZWQVZMk+aTvIKaCiDyllPqO1vqNIrIZgLPhocuqAhxgjFnfdxANudHY6fjhX64FRWRp15p5UrqNwLBR5V+gbEiH84pDkk+51iyKinpiN5u4clKn5/kSJCvr8SsiPpLYz2S3+XWgch0qEyHppRKbZJ0usgDAx/7IttvtJwZZIIck+LRoKrHzh2Sz3ykZZbdOa7fbD0dR9EWt9coA3grgWACPew6rNJA8jOTCvuPoB6XULVrr/ZRSS6dp+g4AF6AZ7jkHIfkl30E0NFQc50lsACt70MyN0HcADXi97wAGYAl4qP4MguAfrjWLoopJbBEZN8Y8D2Axx9IvAbCONatEnVrAAmPMSgDu9x3IdBCRpUjnxQy1eS6i4pXYIvKkB9lWp9NZFsDfPGgXxfIeNB8f9DJIa/2Ete5fVSRf51y05nj6LidoEmTzhGRlfjZa698B+B3JTydJslGapu8UkV0AvMF3bB5ZIkmSjwL4pu9A+kVEYgBXA7ia5IgxZkcR2RXA21HxDsJBEJGdjDHraq3v8B1LQ0MVEZEnPZwbFyM5U0T+7Vp4UEiONElsz5CsbBLbWuvjgIkkSQaqkioZVR0c8iQcJ7GDINgzDMNrXGo2+CN7NlYyiU3S+bORZJ2ei5W+rErT9JEgcN8YoZRaHfVKYr/Jg+Yjgy4gIi8YY14E4LTaUERWdKk3DJB8UkRcy96ptW7a82uEiBDAr7N/PjM+Pr5iq9XaluQuIrIdAO03QreQPJjkt7KfSyURkXEAlwO4nGRgrV03+zx3BvBmAM4fHB4RAAcC+IjvQBoaqghJHxfmSJJkFQCVS2Jba99Qp/bzSiIiq/qOYbqQ9NKG0G63vXzRi4BkVQfAOP8Mut1uMwF7iGi1WpV9NsJDi5anisGiqLQtiogMnAidDiTX8KFbBGNjY8ui1+3lmrw+u7/ntE4/rEBymBInhRMEwT89yC7iQbPBISMjI49orU+LomgXpdRMkrsBOAXAX3zH5ojlkyTZyHcQeSEiqdb6D1EUfVlrvV63210BvaTujwA4txfzxHtIDtVlTENDXpD0sdcAybV96ObAm5sktn+WJ1nJDWsQBD6S2M9lt991oarJGucPWxGp5PekYXqQXNN3DNNFRFZxrUmyTnYiVX0uAgC01o/60K3yd2ZutNZe/i4i8mhOS/lIYo+MjY3VbsCnT7rdrvPLwaqeCRqmh4j8J4qiK7TWH9Fav77b7a4E4H9E5FIAz/qOryhI7uk7hqIYGRl5TGt9htb6vUqpVwFYX0S+AOBGALHf6ApjyW63u63vIBoaKsoDnnQ39KQ7KFs2SWz/BEmSbOA7iOngyQqlTokakKxksoak8wO6iLzatWaDVypZpUNyBsllXOuKSJ2ejZV8Ls5BRF7yVFWxmQfNQuh2u17+Lmma3pvTUl7sfaIoqmpVTSmJoujvAJxaHojIkiQbu8chZU6VtlJqWZoN3gAAIABJREFUj6xKey0AHwNwoYjUyTbsbb4DcEFWpf17pdQ3tNZbKaUWF5EtROSLJH+KGvnfp2m6le8YGhqqiIj4ss+s5FkbwBZNErscVPUX6C2uBUXkQdeaBVPJZI2I3Odak+QKrjUbvLIKycoNykmSZF0Azt+tIvKQa82iqOrl3lzc6UFz+U6n47wLoAhExMthWGt9Wx7reLSUcb4vmy4kFzfGnGSMOchauwlJ18OiF4iIzIZ7n3mVDWltGHJEJI2i6G6t9cla672UUstmldofAnA6Kjo3JONNJH1YRnlFRMaVUr9SSn0tiqIdlVKLo3eePlRELvXljZsTm/oOoKGhiiilHgDgw2L29SSX9qA7beI4fiOA5Zqb/hKQpukmvmPoF5IzrLWru9ZN0/Ru15pFUuFkjfMkNoBmaNVwIcaYjQFc5TuQPnmrD9EwDP/kQ7cgqvpcnMgdAHZ0LRoEwdaouK8qyUWstet5kP6riPwrj4W63e6dPoZ7ojdQrBLEcbxpEASHAABJWGthrf17mqb3isg9AO4FcI9S6n4ReclXnCTvE5EVXGqGYbgCgEddajZUg5GRkUfQ8+7/AQCQnGmM2URENkcvibgugCqc76Xb7W4EYKgHtotIF8Afs39OBIBOp7NqEASbAJjzmVblcvrNJNsi0vEdSENDlRCRMWPMYwBWcC1trd0FvUvRqrAnUI2XXO0Rkc1JLuRzk94vSZK8BUDLg3StktioaLJGKXWftZZwO337TQ61GkqAiLwdFUtik1zfg+xTeSXfSkJVB95OxEclNrIBYaf60M4LY8wuIuJjf/rrvBZK0/QOH0nsNE03Iiki4tQCYzqIyCssY0guKyLLAth+zr+z1tIY84iI3JvZvdwjIvcppe4TERf+svcBeLsDnZchuTp6/rkNDfNFRP4N4MrsH5BcKEmSDdM03SxLbG8EIPIZ47xI03RVDHkSezLa7faDAB4EcDYAkHyNMWbT7Jm5BYA14fb8NVWUMWZFVLtDoKHBCyJyt4+uc5LvQIWS2CLyPqBJYpeFtjFmBwCX+g6kD7y0+pKsU7VhZSuxReSl7MZweYeyM8fHx1fMqlAahoN3kPywiFQiqUkyMMZsKeL8bFGry70gCCr5XJxImqa/b7Xc3/OKyNYkXyMiXiad58QHPOnmlsQeHR193BjzNICl8lpzKojI0tbateHpEqUfsgTblP4ogJVIriQiu8z5l9barjHmryJyd5bcvhfAPVr/v/buPEySqkob+PtG5r2RVY0oiPuAyNbgvg4qiMgqm4gK4jI4iCvqoKPgxqfjqLhvM2644K4gqAwIoiwKgjoqIMomIJsKNCDQQFdV3BsZ5/sjo5m2bbpryYybmfX+nqceoKmKc7qzKyvi3HPP9VeSjH3Ms19z0ucixWKojIG6IeqM+gNmNlGW5fZVVe1Mcj8AWyVN8O9tljqBUVD/PD++/oCZPTCE8CySuwF4LoChGcvSarU2g4rYIvPxCwD7rPOz+qx+btiA5O1Nx56rGOMOK8/k00zsIVHfWIwMM9s7QdipPM//lCDuwExMTIxssYbkRU3HzLJMD3aLy4PKshyZMwNijE8imWK22Fgt7mFEd6isql5suz5B6FZZli9KELcvzOxBJHdJFPu8Pl8ySSGZ5LNTxJ0LM1uChY8+aQHYysyeT/JdJI8jeUmM8e4Qwu9DCMfGGN9ZFMV+MzMzW5jZfFeVGr/XQaKxVDJ+6hnMp+d5/jbv/VL0xo18DMCdiVODmWlM4DyQvDnP8+O894c45x6cZdmeJI/HEOxiq6pKr6nI/JybKG6nLMuXJ4o9J1VVHb7y31XEHh77DuOhNmtiZg9BgkMdAVxSzw4bJ8lvOBbg500HrLsOZBExs39NncNsmVnjK+g1FbGH009TBDWz15nZSO60CyEcijS7BK/O87yvOxrM7MJ+Xm8OcfdKEXcuyrLcDoAb0OU9etvtX2hm7yP5/SzLrowx3jUzM7P5XC/mnPs9gOV9z3Lttp6amtq44ZiyCHjvf+e9f4tzbhOS/wEgpMqF5Eg89w4zkrHdbv/IOXdAVVVLSSbd1U1yg5TxRUZVu93+DYAk8+TN7LVmNtR14aIoHk3ynvvboU52kVkyKqsgMcYDkGYW1zkJYg7ayBZrzOzsBDH3GvY3Wem7F4/CCfb1HNoDUsSuqqrxBaVBGtUxS2twVqK4m8UYR64b28zuQ/L1iWL3/cHfzFJ11Ww3MzOzZaLYs1LPbm/anXmeXz3XLyLZTfBast1up1oUlQUwM18UxTZFUew3zPcuJJc7594D4OkAbkuUxnqJ4s6Jma0fQnhyURTPTZ3L2nQ6naucc/sDeF2qHKqqWpIqtsgoq8/4+E2i8JuHEJI8w87BR7BK/VHFoCFSd08N9WtiZgTw6hSxsyw7M0XcARvZYo1z7kIAdzUZk+SD6w6uoRVjPDKEcFAI4UlmNpk6nzEwWZblIamTWJeyLHcEsDRB6Gs6nc5YjVnCCL8vrqosyzORbrfN20etG7ssy9ci0WzPeit2X+V5fhaAJg4eXB1JDm1TRP33MkUR+6fzPfAyy7IUTRQp/oxmLcb4jBjjW2KMz56ent4kdT5NM7MHxxh3DCG8KoTw0aIofhhCuDLGuILkpSS/H0LYI3We6+K9Pz/LsoMShR+agqeZtWZmZjYvy3KPGOO/hxA+H0I4qyiKG2KMywH8huT3zazRcw7mw3v/WQCfThGb5NC8piKjhuTpCWMfZWZDeQhwURTPX31U3kg94CwCm8UYDwLw1dSJ3JuyLHcAsE2C0KHVaqkTe4iQLIuiOK/p+Ztm9kokGGUyG3Xh+r0r/zvGWIUQriV5ycqDp0he6py7jOR0wlRHipm92cw+T7LRRZO5MLMki3sAxnFxb5THLN1jcnLyryGEX6HX6da0bcqy/DcAH08Qe86mpqYeZmbvTBT+Wufc+f2+aH0A8jkAdu33tWcR+2Azey/JqaZjr0sIYW+SD0gQet7vlWaW4v5zp5mZmS06nc5VCWLPxuErR2i1Wi2EEO5E70C3i0leCuDiGOOlk5OTf0ma5QKYWSfGuNTMtsqybCsz2xq9xeqtYoz3XfVz13SgM8mdAHyrmWznr91unxJCOAvATqlzGTQz2zDGuBWArc1saf26Lo0xbpllma+qv7/9WO11ZQjhWagPWBxmzrn/iDG+DMB9UuciIrNTVdUPSP5novCPKMvyTQA+mCj+GpnZRmVZfsrs73sQVMQeMiTfZ2bHk1yROpc1SfiQ+ath/TNZzEj+BEDTh0jtb2b/TvLWhuOuk5kduNoNbwZgMzPbjOQ9W4NjjN0QwjUkL66q6p6Hvrq4naJzb9g9KITwNgCp3n/WqiiKrQE8P0VsMzsjRdwBm1e35DAi+T0zS1HEhpm9Z3p6+oSJiYkUB0zOiXPuk2a2forYZnbCfDt0Z+FHSFDERu9Q3NcD+HCC2OvymhRBq6qa93gf59xvY4x/A3D/Pqa0LiT5agCHr/MzG1YXAndf7ZfXB7AtgG1XPmy2222EEJYDuBTAxQAuJXlJCOGSJUuW3NBo0msxNTW1sXNuqZndU9wkuVWMcRMAGUms/gA9S/uYWZtk2d+MB+LraL6IPZDDJc3MFUWxGcmtsyxb+bouBbB1jPGeTur5vK4kn4cRKGKT/FsI4RQABzYZ18ySHxgqMqryPL84hHAlgCQj4czsPSGE0733fW/smA8zy0II3yL5sNX/n4rYQ8bMHlYXa/5f6lxWF2Pc1cxSPIyB5Dh2G468brf7vVar9TE0OyO9E0I4HMBbG4y5TmaWxRhfOMtPbwHYwsy2IHnPjL26uH01yT+sLG4DuNR7fznJZIfvDAOSb5qZmflyp9OZ80zTQcuy7AOJRjeY9z7V3GWZhbIsv9dqtT6KNOdIrNdqtY4xs92H+VDkoiheZGYvSBS+MrMvDuziVXVqlmVJuuHN7AgzO5pk04cS3qsQwuMBpDig+Q8L+dlBsgwhnAig0dFWJF9jZh8heXOTcdclxvh89A7QnI37Anha/QEzg3MOIYTbAVyCXoH7EpKXtNvtS0jeNIiczWy9lV3VAJaSXIq6qxrAklWLmWvqqp6nB5RluTOAH/frgoNSVdUvsqzxiZYLKnia2QPLstzazFZ2yy+tu6o3y7KsXX9OfzL9P3ub2Xok7+73hQfgPDRcxM6yTEVskQUwsxNJplq89gC+Y2ZPJpn8ezmEcBTJNd4zqog9hEi+LcZ4mnPuvNS5rGRmPsb40YTxf5gqtty7iYmJ60MI/wvgqU3GJfn6FStWfGqYOnm63e7uADZe4GVaALY0sy1XfYiKMZYhhKvqsST3dDR5769YRMXtiSzLvm1mzyAZUyezUoxxJzNLddjPr0nekii2zMLExMR19UiJZyZKYecQwgcAHJEo/lrVp40PrIi8LiRP7nQ6Vwzq+p1O548hhCvQK5Q17f4xxk8AGJr52CTfU5+t0nTcBXdOkjzBzJo+n2G9GOPbAbyp4bjr0o8/hw0AbF9/wMwQY0QI4TbU9zjo7VK7rN1uX7zQQn6M8b8AHNzHAvVsvRYjUMTO8/ymGJu9tVrIApuZsSzLC83sofW1BlGwXpP1YowvBfD5JoIthJkta/rvu5kNzaKpyCgieQLS7sDaMsZ4opntSXImVRIxxreb2b02LA71IYKLWNvMvmVmG6ROZKUQwvsAPDZR+Eu89xckii3rUL/ZNm3Se//JBHHvVVVVRw7w8m30trg+n+T/I3kcyT/EGO8OIXxlgHGHzbYhhPeu+9OaYWYbmNlXE6bwjYSxZZbM7OiU8Um+JYTwLylzWBMzeyDJ7yPt4V4fG3QAkscMOsZaHFwURZJRR6uLMe5mZs9JEbvb7X53oddot9tnAritD+nM1evqDvahEGPcDb2xIYOyIYAd0Bs782kzOzPGuCyEcEtRFPN+DjGzk/uW4dzi7j0zM5NiEWuuGj/Qy8zmPe+dpCVscDrMzFqJYs9F468pyXE7aFykUd77XwO4KHEazyrL8ttmNtsdV31VF7CPWtvnqIg9vB4eY/yBmXVSJxJj3J3km1PFJ6lCzRAry/IEJDiIzcz2L4pi/6bjrkmMcRekObzNATg7QdxkSB4RQvjX1HmYWVaW5Zex8O77+QrOuWMTxZY58N5/D0DKcQAE8JWiKGY77mjgzGyjGOMZSDT3r/Zr59zADwlut9tfBZBs9wjJLxVF8ZhU8YHeKAcz+1yi8Bd1Op0/LvQi9Q6gE/uQz1w5AF8xM5cg9j8ws1TjDu/y3l883y/23p8OIEVXWSvLsqFZfL83McZ/mDnagEsX8sVZliVZmEBvtvZBiWLPWpZljb+mIYRLmo4pMoaS7/Qws/1ijD80s8YOhzUzF0L44roK2ICK2MPumfUqSLLV3hDCk83seKT7u9KNMX4zUWyZhYmJievMLMlWSZJHz8zMLE0ReyUzy8ws1UnCdzrnhv6AmT4jgC+WZbl3yiRijB83s/1SxSd5Csm/pYovs0cymNmXE6fRIvnNoihekjgPmNlDYoynA0hdWB14FzYAkFxG8qQmYt2L+2VZdtr09PTDUwSvt/0fA2CzFPFJfquPl0u1q+LxMcZPJYp9j7Is90A9/qNpJI8hOe+GCZJ3m9lP+5nTHOwfY2z60MS5SrHbdkFF7FardSaAFX3KZU7M7Cgz2zBF7Nkys6Zf0zuGacyjyKhyzn0LwF2p8wCwa4zx3KIoth50oJmZmc1jjD8D8IrZfL6K2EPOzPYry/L4FB3ZdefOqQAaW4FZg7MmJyf/mjC+zEKr1frvRKE3yLLsh2Z2/0TxUZbl4agPLErgOyST3MAn1q6q6oSiKJIcBlcUxXsAHJYi9kpVVWmHygipxx+l/l5tk/xGURTvTTGXGOgd7FeW5a8BpB6PcL73vslRWMnmfgOAmT201Wr9vCiKRzUdO4TwQTNLtWtqpt1u922cS73N93/7db05em0I4d8SxYaZbTjIQ1DXoRtj/GofrvO1PlxjPmhmXzSz+yaKv05m1vROncI594eFXIDkNIAkjRwkH1zPWR9KZjYJYJ+Gw57fcDyRsUTyLgzPyMjHkjw/hHDoIBpr6+7rw7Isuwhz2NWuIvYIqNv5f9xkoS7G+EyS5wB4QFMx18TMvpQyvsxOq9U6DcDADsdahy1ijKeb2UZNBw4hPCFhF7aZ2WcSxR4GOcnjQghvaCqgmbVCCJ8l+a6mYq4JyRu896ekzEHmpj6ULPn2QAAkeWQI4WQze0hTQc2MIYRXAzjXzP6pqbj3lg7JwxbS1TlX7Xb7dADznv/aJxuTPLfuph24+v3y4yRTHip6bL93rJhZqkV7APhECOGVKQKXZfl5M0sxcgIkfzA5OfmXhV7He38igFSHIW9WluXXUi0grk1RFNuQ3K3hsOf2owmD5Bf6kcw8vSSEcGjC+PcqxvhyNNyERvK0JuOJjLNut/sRACF1HrVJAJ+JMV4QY3x2P36OmZkviuKlMcbLAHwSczwfR0Xs0bFDWZa/izHuMMgg9YPmYfV4iPsNMtYsXFHPEpUhR9IApCyoPiHGeNbU1FRj84nrWMcDSHLogZmdkuf5grpYxkAG4L9CCMcOusPJzB4SQjgVwGsHGWeWPkZyWG5sZJaccx8BMJU6DwAguVeM8eIQwsvMbKD3gjMzM5uHEE5Dr4if8hDHlY51zp3XZECSlZm9p8mY9+J+VVWdEkL4dN2pNxBTU1MPizGeBuBNg4oxS5/u9wW998eb2U39vu4sZQCODiE0+udaH7KU7AwSM/tQP65DsjCzVN3YMLN9Y4yfHqZCtpm1SH4FvdnrjSH5o35cxzn3SwDznpXeB58qiuJ5CeP/g+np6U0BfKDpuFVV9eU1FRFgYmLiWgApDwZfk8ea2Y9ijJeGEA6b65i6urlh26IoPhxj/HN97t3m80lERewRYmb/ZGZnhRA+YWYb9Pv69YPmqeithjR+ovEafIBkN3USMjvOuS8nfLADgMe02+3fxhifOehA09PTm7Tb7Z9hnm+8/ZBlWV8e6sbEC2OMvyuK4vn9vrCZsSiKF8UYL0rQqbQmt7bb7VRzWWUBSC4zs4+kzmMVGwL4aozxwpmZmX36XViZmpp6WAjhs1mWXTYk3zsAMFWW5VtTBPbefxtpiy0rEcDryrK8IoTwKjNr9+vCZpaHEA5rt9uXANilX9edp3O9933f3k4yZFn20X5fdy4pAPh4COGrTYwajDG+czaHLA3QGd773/brYmb2eQBlv643D4fGGD/fz++7+TKzVozxywC2bTp2VVX93E3W98WqOWiT/E5RFC9NmMM9zOzBrVbrZADrNRz62jzPdaijSB+VZfl+AEXqPNZgawCfbLVa14YQLgkhHBNjPKIoigNijLuGEJ4aY9ylKIp9QwiHFEXx4aIofhhjvAXAr0geDuCBC0lARezR0wLwxhjjlTHGw/tRzK4fND+WZdmlJJ/dhxz74dp6qL2MCJIrSKY+gf2BZnZmCOFjZjaQjr8Y4zNardY5SHRIVe3nzrlzE8YfRpuSPCGEcHaMcbeFFuTMjDHGnWOMvyD5bSQerbQSyU8u0jnoY8F7/yEA16XOYzWPzbLspBjjFTHGdyzkAEAz68zMzOwTYzyx3W5fg97OhUY7/NaG5FGTk5N/ThS7MrP/lyL2mtSjIY6OMV5dFMV7pqenHzHfa61YseKhMcZ3xBivRK8RIvns30GOfWq3258BcP2grj9LL4sxXhhjfMYgLm5mkyGE/zaz9w3i+rNFsq8dpZ1O508AUh8Y/6oY44+npqaSjGcBADO7b1mW3wXwsgThz8vz/PJ+Xcw59xWk/X70JL9eFMWHzSxZE1hRFNvEGM8B8OimY5NMfXi1yNipx2gNe+PSIwEcbGYfInmcmf0EwC/N7HSSJwL4EsnDSe4FoG9NuEOznWkYFEXxAZJvS53HHE0BONbMTvLenzHb4oaZbRBC2DXLsgPNbB8AyTsCVnOo9/5zTQYMIZwJoNHTw51z69fD+8eCmbkY46UAtkidC3qFovc5575GMi70Yma2XozxKACvQ9oFQAPwdO/9r5oKGEJYDmD9puL1yR9Jft3MTnXOXVSPvFmnoigenWXZnmb2cgBLB5zjXN3hnNuU5PKmAhZFsTXJy5qKV/uG9/6ghmM2piiK55Ns8lDB+bgCwBkAfkfyina7fQWAu1f+vDIzB2C9EMI/Adgqy7KtzeyZALYHMJEs67U7zzm3I8lkXZj14tivAPxzqhzW4Y8AzgJwfv26XwngrlXvLc3sATHGh5rZUgBPIfksAE/EcD1TnOG933WQAUIIB2M4tvoagG+b2fv6VRiMMe5Yn0mTbLcZAJjZaXme932G+8zMzOZZll2G9AtstwN4h3PuS029L5lZFkI4MMuyj5jZQ5uIuYYcXpzn+Xf6ec0QwqswHMWe35N8o3Pup00FNLP1Qwhvqc8fSFFED865h5NsZDduCOEJAC5oItYqvui9f1WTAc3sgTHGZQ3HPCXP872bjDkfMcYfmNlzm4xZluXG/TibYa7MbIN6bvSDmo49zIbphjO5ES1ir6pAb6vqJSSvNLM7ANyJ3ut8H5Ib1g8d2wB4LHpd3cPocufc40k2un1CRez+KIrihSSPTZ3HKq43s6+Y2Tc7nc6cD9aamZnZLMuyQwEcgvRz4gHgWO/9i5oMOKJF7FUtA/B7AJeS/KuZLTez5STvS/I+ZvYQM3tklmWPTXVw1WyQPNw51+g2dhWxByPGeKKZ7Zs6j3kqMXwL3+tye7fbfcLExETyLvgY43Zmdg5GbzdkRPqi32w9bdALvfUohosAPGqQceagInlyVVVf996fMtd7aDNzIYT9SL4BvcWo1Eoze/ygRhSEEL4I4BWDuPY8XAHgk865b5C8exABzOx+McYDAbwBvc65VJY55zbp97keZubrQk/KXZKrOj3Lsk+2Wq3TBnWI8MzMzOYkDyb5WvTGg6XS6HOJitgDjaki9r1IVcQGgKIoXlTvCpaaitirGIMi9jgwkjs5537WdGAVsfuj7jT7KYCBz6aehz8COBvAb0le1e12r8/z/DYAKwBMzMzMbNhqtTYysy1IPh3AdgAeh+EpNsx0u91t6sMeGjMGRexxcJFz7slNd5GqiD0YdTfrH6DOikaY2f55ng9N93sI4WMA/j11HuOI5PHOuQOaiBVj3NnMTsfwPU+tMLNzsyz7eVVVl5O8yjm3DMB0/XH/EMKGWZY9AMBTAGxvZtsBuH/KpFfzee/9wA5Srt+DL8Nw/Z5XkDy1qqqTqqr6+UIW3cysHWN8NIDtzGxPkjsBGPgM9Vl4o/f+U4O4cFmWe/Z51vaC1Y0TP8iy7EetVusXJO+Y77XM7D5lWT6hqqqdSO4J4MlI/97TNbPHNTkPW0XsgcZUEftepCxiA0BRFD8eovNlkhu1ThoZf19LUcCW/iFpMzMzr8iy7CIAk6nzWc3S+uNVZoYsyxDj/00aabV6mxPI1PeEa0byvU0XsGUoVABek3IMgvQXyVvKsnxFVVUnIf1D6Lj7wjAVsAHAOXdkjHEvDN/IolF3R7vdPqypYM65M+uO3kaLG7OwhOTuZrb7yvuZVe91gN59jtmspmw1zsxu8t4fOcgYJG8JIRwBYJhm+S4xs/1J7t9qtVAUxU0kLwVwpZndlmXZcjO73czuRL0zguT6JNevv3Z9kg83s81jjFujLloP0T3tlc65gY2KbLfbp8YYjzez/QcVY67q3X2vr6rq9VVVVSGEq83scpJ/MrPlWZbduXJ3IHq7KSbR+/69b1VV9yF5XzPbnOQWMcbNAWRD9HoCwJd0oKPI4JnZoSQvBHCf1LkMAxWxZZjc6pw7PHUSsnCdTueqEMKRAD6eOpcx8tt2u/3h1ElIEkc3OQNdmtFut39YFMVH61O6ZTDOc869MXUSqyM5HWN8eT1WZFhHu42iI0je2GRA59zhMcY9AGzcZNwx91qSfxt0EOfcV2KMB2E4dw6C5IMBPBjATqsuOqxexFx1MWJYFyYAwMze2u8xIqtrt9uHxRh3xXCM/1tdBmALklsAf7+QtKbXdOWvDVnRelV3OefenToJkcWg0+n8qSiKQ0h+N3UuQ2B6WLbIi8DMDiN5a+o8pD+cc58C8MvUeYyJwswOVifuonSdc+7tqZOQwfDev93MTk2dx5i63Dm3L8np1ImsiXPuF2amhcn++Zlz7ktNByV5Z5Zlr2467hj7dp7nJzYRiKR1u92DAAy8YL7YkTwpz/MfNBDnRjN7BXoHnspgvZVko+MuRBazPM+PBzCQcUyjxMxeqSK2DIvP5HmugfVjhGTV7XZfAuC21LmMgSPyPL84dRLSuBkAzye5PHUiMhgku977FwNoeu74WDOzG7vd7h5NdHMuhPf+SJInpc5jDNxcluW/kExSuGq32z8C8IkUscfM1c651zcZcGJi4vosy/4FvbFdMgBmdmO73W7sEM08z78HFXoGysx+6Jz7fOo8RBabemrBL1LnkYqZfTTP82+piC3D4BfOOR1wNIYmJiauIfkS6OFgIb7pvf+v1ElIEod6789PnYQMFsnlZVnuBuCa1LmMibtI7jUK5weQrNrt9ksA/C51LiOsS/LFKQ9cAgDn3BEAzkmZw4hbYWb7kby96cDtdvtHZvaBpuMuEpZl2cEkb2kyaP39qN2gg7HMe39IqkVDkcWMZCzL8oUAbk6dS9PM7Cfe+7cBvdlMktai7lI1s5tijPsPekaapOOcO43kW1PnMaIudM4N24FRTbgbQJE6icSO9t5/JXUS0ozJycm/dLvdnUkmLcSNgbtIPsd7f2HqRGaL5N1lWT7HzBqd5TwuSL7bOXfmEORROuf2B3B16lxGkZm9Ms/z36eK771/F4Bvpoo/rkj+h3PuxwniRufcvgD+2HTsMVeQPIDkoiugiQyLetF+b/SelxeLy7z3B5LsAipiJ2eZW8kPAAAXhklEQVRmrwawWDvtprIse8GSJUtuSJ2IDJZz7qPQ1r65urYsy32GdZ7rgN1QVdX+ABbr4tbZzrnDUichzZqYmLim2+3uDOC61LmMqFsB7OSc+1nqROZqcnLyzySfA+CO1LmMmK+22+2jUiexEsmbq6p6NoBGu05HHckj8zz/TuIcKufcy83slJR5jJljnHP/mSo4yVu63e5uAP6cKocxU5nZy5xz2nEikpj3/jckn4fF0fT1R+fczqvu1FIRO7Esy+5wzu0K4ILUuTRshuRznXPnpU5EmlGPjPlG6jxGgZndVFXVrpOTk39NnUsqnU7nZDM7AIuvkP1L59w+JBfDTYmsptPpXBFjfDqAi1LnMmL+bGY7eO9/mzqR+apz3xm9YrysA8nvO+deMWxb2judzpUA9oIWJGbFzD7qnHt/6jyAXveu9/4AAD9LncuoM7PTnHPJDzydmJi43sx2N7ObUucy6ki+Nc/z41LnISI9zrnTsyx7HnpnKI2rK2OMO5H8u92KKmIPAZK314Xskdn+ukAhy7IXOOdOT52INKfucvlXABqRsHa3Atit0+lclTqR1PI8/x8zeyGAmDqXhvzWObcHybtSJyLpLFmy5Abn3A4A9DNydv7Y7Xa3z/N85A/H9N5fYGY7arTI2pnZT9rt9otXbisdNt773wDYBYt8ZOAsHO29PyJ1EqsiOVX/HP5B6lxGlZn92Hv/fJJl6lwAIM/zy8xsOwCL/r56vkgeWe+qFZEh0m63TyW5L4AVqXMZgKvKsnzWmqY2qIg9JEjetkg6skszO7Ddbmu73iJUF7JfAeCTqXMZUtdWVbV9nud/SJ3IsMjz/EQzOxDjv13qIufc7iSXp05E0iN5p3NuDzN7P3Qw7r0ys1Occ9tNTExcnzqXfsnz/BIz2wEaK7NGJE/w3u877LtVvPfnm9mzNOd+zczsA8651w5bJz0AkJxpt9v7A/hC6lxGDcnvee+fQ3IqdS6r6nQ6Vzvntsf4P2f3mwF4w7DslhCRf+Sc+wmAHcasAeJXMcZn3tuudBWxhwjJvznndjSzH6XOZUCWk9wrz3N1NyxiJCvv/ZsAvAHAUHZRJXKRc+7pnU5Hh9CsJs/z75PcHePb1XaWc+5ZJMf19yfzQLKb5/mRJPeERkysriT5Vu/9PiT/ljqZfut0Olc557YF8NPUuQyZT7bb7ReSHImts3me/z6EsC0Wz07L2agAvCHP83cMYwF7JZJd7/2rSb4Zi2c32EJ9pf7+HMoxcCSXOed2BKCRGLMTARzsvf906kREZO289xd0u91tAfw6dS598GXn3I5rOzdPRewhQ/Iu7/1zABydOpc+u87Mtq9XikTgvf80yWcDWJY6lyFwnHPuGavPe5L/45w7u94Oek3qXPrsGOfcs1c9rEJkVc65HzvnHg3gu6lzGQYk/0LyWc65Dw9zEWyh6oLLrmZ2FHrdcItZAPBv3vs3kRypnQn1eKBnAPh66lyGwN+yLNt7lIpizrmPk9wROhxwbQoAr/Pev3xYR/ysVD9nHwjg9Rj/HX4LcT2AHbz3X0udiIjMzuTk5J/r+43/xmjeN0YAr/fev2Jdu+1UxB5CJEvv/WtIvg2j+Rdwdb92zj01z/OLUyciw8U5d4Zz7gkAzkqdSyIFem/WB2oO8rrleX65c+5pGI9VZiP5du/9ISTV5SVrRXKZ9/6FVVXtS3LRHvgK4Nh2u/1E59y5qRNpQt2N/84sy/YGMHYd57N0OYCnee//O3Ui80Vyhff+ZQAOAXB36nwS+VW3231iu90eud2mzrlfOOeeqDnZa3Q1gO28959NnchceO8/A2B7AJemzmXYmNmpzrkneu9/lToXEZkbksF7/2/1DuZRGrX3ewDb1+/N66Qi9hBzzn2ofnC5OXUu81TVp44/k6ROhZY1Inmjc24XAK8BcGfqfBp0IYCnz/bNWnpW2Q76udS5LMCfSe7mnPtg6kRktHQ6nZPa7fZSku8CsJgWvi4jubP3/kUkb0mdTNPa7fapzrnHAPg2xqO5YTYMwBecc0/y3o/FHFvv/THdbvcxWFyHtgYz+0/n3A6jPLue5K3OuedVVbUvRqswMCgVgKPr78/zUyczH9773zrnnkDySADTqfMZAncAeIP3fu9xHNMlspg45053zj3GzD6K3m62YTVF8q31z5JZN6mpiD3k6geXx2H0bnivIfmsPM8PH5XZhZIOSfPeH12W5aMAfAvj/ZC+guSbnXP/PC4P5k0jOe29P9TM9sPodSd+wzn3WOfcGakTkdFEcoVz7r3OuS0AfBrjvSX67vrm9nHOucW6YwdAb8HXe/8SkjsBuCR1PgP2W5LPqGcSD9UBcQs1MTFxrXNudzP7F4z/iIpzzOzxeZ6/e1x2HHU6nZOcc48ys49g8RY+fwngKfWu4TtSJ7MQJINz7v1VVT2W5MkY7+ePe1OhN9puaT3qcTH+GYiMHZJ35nl+eFVVjwFwLIbroPiK5A+63e6j6/GA5Vy+WEXsEUDyJufc7iSPADDsBeESwOfqB85zUicjo2VycvIv3vuXAngqxu9AqxLAl8uy3KaesTinN2v5R3men1iW5eMxAuNoSP7FzF7gvT9o1B/6ZDiQvNl7/wbn3KZm9kEAy1Pn1Ed3A/h4jHFpfXM7FgWwfnDO/azuHnwLgLHqSjezGwEc7Jzb1jl3Xup8BoWk5Xn+Tefc1nUX6LidiXCFmb3EObdjnueXpU6m30jenef5Ec65zQB8AsBYLbSsxWUADnLObTduTRj1YbrPAfCURVTMNjM7Db1xTYeQHNWd3yKyFp1O5wrv/YvM7FEAjkHaBdgA4Bgze7Rz7nkTExPzOutKRewRQdKccx/pdruPJHlC6nzWhOSJZvZY7/2hmu8rC+G9/7X3fieS25nZKRjtm8kSwDeqqtrGe/+KycnJce+8atTk5ORfnHO7mNkLAVybOp81uJ3kW9vt9lZ5nn8vdTIyfkjelOf5251zmwB4I0Z7xuctJN/lnHu49/7NazuZfDEjGZ1zH3POPRzAoQCuSp3TAl0G4JXe+828918dtcMb54vklHPu/c65TUm+HaN/0PXV6C1CPCrP82+Pe0cnyZu89//unNvMzI6qF2HG0dlZlu3jnHuU9/4b4/y6eu/Pr4vZTwbwDYxnt30A8FUze1ye53vMZQu/iIyuPM8v994f4px7GHrPC/+L5mosfzKzo2KMj/DeH7LQBW4VsUfMxMTENc65/Uk+E8CwzCD7KcntnHP7jWPHhaTjnPtFnud7V1W1tJ7pNDJdZyRvMLP3lGW5qff+oE6nM+pFhqFVd7V91zm3Dcl3YjhmBd9pZh9xzm1Rd5KO44OQDBGSd3rvP+W9fxTJ7QF8FaPRnW3obU9/nXNuU+fce0neljqpUVCPVvqcc25rM9sfwHkYnUXfaGanZlm2V10c+9JiHT9H8k7n3Aedc5uY2YHo7UQbldexMrNT6iLnlvUixKLaaUZyWZ7n7/Teb1Ifvnsyeg0MI6suyP8XgCd773dst9s/HOfi9eq89xd47w9yzj0UwOvQO8dm1F1E8h11EengPM//kDohEWkeydvr54WndrvdzQC8juSJ6O+uMANwAcl31U2uW+R5/s5+Nae0+3ERaZ5z7hwze0pZljsCONTMnotmX8+/Afi6mX0hz/PLG4wri1Cn07kSwOFm9o5ut7tLVVUHANgXwAaJU1vd7QBOrqrqe3men7rYHuRSqwsgR5nZZ2KMB6HXobh1w2n8Br3DyI4leXfDsUUAAPUohvPM7DVFUeyWZdkLAOyD4XrP/B3JY8uyPG5iYuLa1MmMMpJdACcAOGFqamrjdrv9AgAHANgWAJMm9/cqAD8H8B3n3Ak6POzvkQwAjgNw3PT09MPb7fb+ZnYAel2hw/Q6Gnpzy79fluWx+v7tqe/5TgJwkpndP4SwB8l9AOwO4L5ps1s3M7uR5Mkkj3XOnVO/ryxq9fi3zwL47MzMzJatVmsfM9sbwDMw/HUUA3CJmX0PwHFqNBOR1dU/vz8L4LNmxqIoNif5pCzLtjazLQFsCmAjAA/EPz5D3IFes8ydAG5D77yWi0j+od1uXzzIyQzDdEOUXFEUHyD5tiZjkty1Hwd8TU1NPcw5d7CZ7QXgKQBaC8/uHywD8BMzO8V7/z/j1jFjZvdB8zckdyymzoZ+MrNWjPEpJHczs53Qe8hb0nAaJYALAJxH8kftdvtn4zi7NYSwHMD6DYa8wnu/dKEXMTOWZbmTmb2Y5LPN7KH9SG41FYALzOwnJI/33v9uADGSMbMWmn3tASCQXNFwzLFXv2c+ieSuZrYLevcKTb5nXo9eAfO8qqrO7HQ6VzQYe1Ganp7epNVqPQfA0wFsB2CThlMw9B5qfmpmZ3vvzyZ5a8M5jDwze1AIYReSu6H3Wm6RIIebsiw718zOLsvyfzQabfbMzJVl+XT0Xrttq6raluSDU+eF3nvy2QDOqarqHL0nz56Z3S+EsCOAp5HcFmmeQVZXAfgDgLPN7Bzv/TkkR2YH62wlui8tmj5k2MwI4H5NxgQQR6EBx8zWA+AaDrt8sYw6WxczWy/13xMVsVcxykXsVdWr/7uS3BnAo9HrRJzrm2AB4HIAfyR5vpn9xDl3kQquMqzqAs2jATzZzLYG8EiSWwHYGEDehxC3oTe38zKSlwO4oN1u/3oxFNtGtYi9uqIoHptl2bMBPNXMHglgc8x94epm1H8P6oeEM8bxIUHGn5m1QgiPJPkUAE8ws61IboleoXMhC+F3AfgTyaurqroawIXdbvfnKnqlNzU19bBWq/V0ktuS3MLMNgXwCCz8/d3Qe2+8Er2i9cUkL2232xep27r/zGyjbrf7z2b2GDPbBsA26L2OD+jD5UsA15C8pKqqy9Hr5PyVRqL11/T09MNJPq7Vam1Zd7ut/Hgo+tuINAXgJvTmlV8K4FKSl7Xb7Uu1oNQ/K59BzGybLMu2NLOt0Hs9N0N/vi9XdRuAZSQvX/k9SvIy59zlTRdaRURSUBF7FeNSxF4TM3tIWZZbVlX1QJLrAbhP/U+a2d0A7jazu7Is+1u3270uz/NrtY1MxoWZPSjG+BCSG1VVtQHJ+5G8L+oHhaqqJrMsm6o/947eP+zWLMuWdbvdm/M8v2ExFKvvzbgUsVdnZi6EsGWr1dq02+2uV/+9WB9AB715rXeY2d1mdler1bql3W7/UbN6ZdyZmZ+ZmXlIq9V6cJZlD6iq6gEA8vo9E+gtChb1e+WMmU1lWbbczJY7567Wos7oMbMNY4yPqH9Grk9yAkCH5P3Qe71L9GYf3wWgNLMVrVZreVVVN8YYb5ycnLxZ47PSM7NOURQbt1qtB3W73Q2yLLsfgPXr+33g/zrXopnNoHdo3QzJm6uqusF7fwuAm3X/n5aZbRRC2Kh+/92I5AboLbh7kksAoKqqiSzLpnufbnfUX3cXgOksy27pdrvL8jy/cTHfuw4LM3MAVn9NVzaXTZLM0XseaQEIZhYArKi/9vZWqzVVVdWysixvmpiYuLkeOyQismgN+ywn6ROSNwIY11OzRdaK5DL0xuGI3KMe/XJp/SEiuGcu73X1hywC9eKcFuhGXD3m78r6Q0ZU3SF9K3o7YmXE1feaeg4XEemTLHUCIiIiIiIiIiIiIiL3RkVsERERERERERERERlKZnYfFbFFREREREREREREZCjNzMzcXzOxRURERERERERERBKIMe4G4NVmdh2A68zsWpLXO+euq88vWfTa7fbmKmKLiIiIiIiIiIiIJGBmmwB43sr/JgkAiDEihDAD4AYAVwO42sxuJHkDyau73e7VeZ5fR7KbJPEGmdlWKmKLiIiIiIiIiIiIpJGv5f91AGxWf9xT4DYzZFmGGGMRQrgewHUArie5spP7urIsr+t0On8lGQf9G2jAI1XEFhEREREREREREUnDL+BrcwBb1h8wM6z8Z6vVQoyxG0K4Ab0i97Vmdh3J60heX1XVdd7760hOL/Q30ICdVMQWERERERERERERSaMY4LVbADauP7ZftZOb5MqRJcvM7Og8z989wDzmbXp6elMA22SpExERERERERERERFZpO5KHP9BJPdJnMO9arVabwRAFbFFREREREREREREEjCz1EVsAHi0mU2kTmJ1ZrYRgFcAgIrYIiIiIiIiIiIiIglkWXZr6hwAuBDCnqmTWF2M8aMAlgAqYouIiIiIiIiIiIgk0W63L0+dAwBkWXZQ6hxWVZblXgBetvK/VcQWERERERERERERSYDkrQBuSZ2Hme0ZQnh86jwAIITwhKqqvrXqr6mILSIiIiIiIiIiIpLOpakTANAG8Hkza6dMIoTwFACnAbjvqr+uIraIiIiIiIiIiIhIIiR/mjqH2rZlWX7XzHzTgc2sFWN8C4BzATxw9f+vIraIiIiIiIiIiIhIImb249Q5rGRm+8UYzy6K4lENxfNFUbw4xn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 <text x="247.03479923518165" y="454.0" font-size="14.0" fill="#e0e0e0" font-family="'Helvetica Neue', Helvetica, Arial, sans-serif" dy="0.3em">Alignment and Postprocessing</text>
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diff --git a/docs/images/metro_map_light.svg b/docs/images/metro_map_light.svg
index 49817c64..ac3be697 100644
--- a/docs/images/metro_map_light.svg
+++ b/docs/images/metro_map_light.svg
@@ -3,97 +3,102 @@
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+<style>@media (prefers-color-scheme: dark) {
+    .nf-metro-section-label { fill: #d0d0d0; }
+    .nf-metro-section-num-circle { fill: #777777; }
+    .nf-metro-title { fill: #ffffff; }
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index b416b9e6..dd5620b1 100644
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