config_parse.yaml

# Email to which errors will be sent
email: ""
#The ID for the alignment (if we run alignment multiple times [e.g. for different genomes], use different IDs for each run)
align_id: "run_1"
#Location of the config file for splitcode's barcodeIdentification
bID: "config_parse.txt"
#The "prefix" of the tags (within each tag group) in the order that they are expected to appear; omit to skip ligation efficiency calculation
tag_prefix_order: "pbs,pbs,rt_pbs"
# Path to samples JSON file, e.g. as produced with the fastq2json.py script (note: don't put spaces in sample names)
samples: "example_samples.json"
#Location of the samples in FASTQ read format (*_R1.fastq.gz and *_R2.fastq.gz)
fastqs: "./"
#Output directory
output_dir: "out_dir/"
#Conda environment: either a path to a conda environment YAML file or the name of an existing conda environment
conda_env: "envs/swiftseq.yaml"
# Scripts directory
scripts_dir: "scripts/"
#Index adapter trimming sequences
R1_adapter: "AGATCGGAAGAGCACACGTCTGAACTCCAGTCA"
R2_adapter: "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT"
#UMI extraction pattern
umi_extraction_pattern: "0:0<R1>0:-1,1:0<R1>1:-1,2:0<R1>2:-1,3:0<R2>3:-1"
#Genome FASTA and GTF files (not necessary if index is already created and supplied)
genome_fasta: "/home/dsullivan/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz"
genome_gtf: "/home/dsullivan/Mus_musculus.GRCm38.102.gtf.gz"
#VCF file to look at polymorphisms for allele-specific mapping (set use_vcf_wasp_workflow to False to skip variant workflow altogether)
vcf_file: "/groups/guttman/delaney/swiftseq_files/CAST_EiJ_and_129S1_SvImJ_SNPs_mm10.vcf"
#Species (i.e. column names in VCF file) which we'll use for allele-specific mapping; Set vcf_s1 to REF when one haploid is the REF (reference) species; Leave vcf_s1 blank to use your VCF file directly without preprocessing
vcf_s1: "129S1_SvImJ"
vcf_s2: "CAST_EiJ"
#Enable the STAR+WASP workflow for allele-specific mapping, making use of the VCF information supplied
use_vcf_wasp_workflow: True
#Whether to perform an additional analysis for each species by splitting up the reads by species
split_allele_analysis: True
#Eliminate certain reads (e.g. rRNAs, repeats, etc.) from the mapping process based on alignment to supplied FASTA file; set use_exclusion_filter to False to eliminate this procedure altogether
exclusion_fasta_file: "/groups/guttman/delaney/swiftseq_files/mm10_ncRNA.fa"
use_exclusion_filter: True
#Merge anndata files from multiple samples into a single anndata object (merged.h5ad)
merge_anndata: True
#Use STAR/STARsolo alignment
use_star: True
#STAR index (leave blank to generate a new index from supplied genome fasta and genome gtf)
star_index: "/groups/guttman/delaney/swiftseq_files/star_mm10_index"
#Additional parameters to supply to STAR alignment; e.g. for outputting unmapped reads, set: --outReadsUnmapped Fastx
star_additional_params: ""
#Features options (note: "features_start" is relative to the end of barcode+UMI; e.g. position 0 means search right after the UMI; features_length is length of search window)
features_fasta_file: "/home/dsullivan/CRISPRi_v2_mouse.trim_1_29_forward.fa"
features_start: 0
features_length: 45
features_kmer_length: 27
use_features: False
#Use kallisto for mapping reads to reference
use_kallisto: False
#kallisto index files (leave blank to generate a new index from supplied genome fasta and genome gtf)
kallisto_index: "/home/dsullivan/upload2/mouse_nac/index.idx"
kallisto_index_t2g: "/home/dsullivan/upload2/mouse_nac/t2g.txt"
kallisto_index_c1: "/home/dsullivan/upload2/mouse_nac/cdna.txt"
kallisto_index_c2: "/home/dsullivan/upload2/mouse_nac/nascent.txt"
#D-list (i.e. background filter) to use when generating a NEW kallisto index (if blank, will be automatically set to the genome fasta)
kallisto_dlist: ""
#Specify number of reads to subset (set subset_n to 0 to disable) and the directory (temp_dir) to store those subsetted reads
subset_n: 0
temp_dir: "tmp/"
#Load balancing
thread_factor: 1
num_chunks: 1
keep_fastq_chunks: False