OpenOmicsBench

OpenOmicsBench is a compact benchmark collection and local sequence toolkit for bioinformatics software testing, method checks and teaching. Version 2 keeps the 12 certified bulk RNA-seq objects from version 1 and adds strict FASTA and FASTQ handling, DNA, RNA and protein support, paired-read checks, preprocessing, sequence QC and five deterministic sequence benchmarks.

Version 2.2 adds multi-method benchmark matrices, report-to-report regression gates and portable benchmark bundles. Suite reports can be written as JSON, Markdown, JUnit XML, CSV or a self-contained HTML page.

The package runs offline after installation. Sequence files stay on the local computer. The built-in tools cover inspection and lightweight preprocessing; they do not claim to replace aligners, variant callers, taxonomic classifiers or assay-specific statistical workflows.

Install

OpenOmicsBench requires Python 3.11 or newer.

python -m pip install openomicsbench
omicsbench --version

List and validate the bundled benchmarks:

omicsbench list
omicsbench validate rnaseq-002
omicsbench validate sequence-004

The wheel contains the complete collection. A repository checkout and network connection are not required.

Sequence tools

Run a strict combined QC report on FASTA, FASTQ or gzip-compressed input:

omicsbench seq qc reads.fastq.gz --molecule dna --adapter AGATCGGAAGAGC

Common operations include:

omicsbench seq stats reference.fasta.gz
omicsbench seq validate proteins.fasta --molecule protein
omicsbench seq pair-check sample_R1.fastq.gz sample_R2.fastq.gz
omicsbench seq trim reads.fastq.gz trimmed.fastq.gz --quality 20 --min-length 30
omicsbench seq filter trimmed.fastq.gz clean.fastq.gz --min-length 30 --max-ambiguity 0.05 --min-mean-quality 25
omicsbench seq sample clean.fastq.gz subset.fastq.gz --count 10000 --seed 7
omicsbench seq transform transcripts.fasta proteins.fasta --operation translate --frame 1
omicsbench seq protein-stats proteins.fasta --ph 7.4
omicsbench seq digest proteins.fasta --enzyme trypsin --missed-cleavages 1
omicsbench seq motif reference.fasta ACGTRG --both-strands
omicsbench seq kmers reads.fastq.gz --k 21 --canonical --top 50
omicsbench seq compare sample-a.fasta sample-b.fasta --k 21 --size 5000

The command suite supports:

• multiline FASTA and FASTQ, including .gz input and output;
• DNA, RNA and protein alphabets with IUPAC ambiguity symbols;
• sequence length, N50/L50, composition, GC, ambiguity and exact duplication statistics;
• FASTQ Phred+33 range checks, Q20/Q30 summaries and per-position quality;
• paired-read identity checks, interleaving and deinterleaving;
• fixed, adapter and end-quality trimming;
• filtering by length, GC, ambiguity and mean quality;
• deterministic sampling and deduplication;
• reverse complements, transcription, back-transcription and six translation frames;
• overlapping IUPAC motif searches and six-frame ORF discovery;
• canonical k-mer counts and deterministic bottom-k similarity sketches;
• amino-acid composition, molecular weight, hydropathy, charge, estimated pI, aromaticity and extinction estimates;
• trypsin, Lys-C, Arg-C and chymotrypsin digestion with missed-cleavage and peptide-length controls;
• record extraction with zero-based, half-open slices.

Commands that write files refuse to replace an existing output unless --force is supplied. Outputs are written through a temporary file and moved into place only after the operation succeeds. JSON reports go to standard output so they can be stored or checked in automated workflows.

The FASTA validator follows the nucleotide symbol expectations described by NCBI. FASTQ has no single formal specification; OpenOmicsBench accepts conventional multiline records with printable Phred+33 quality characters and requires the sequence and quality lengths to match. The GA4GH-maintained HTS specifications describe the surrounding SAM, BAM, CRAM and VCF ecosystem.

The full command reference is in docs/sequence-tools.md.

Benchmark suites

Validate the complete installed collection and save reports that can be attached to a CI run:

omicsbench suite validate --json reports/validation.json --markdown reports/validation.md --junit reports/validation.xml

The selection can be narrowed by assay or by repeating --id:

omicsbench suite validate --assay bulk_rna_seq
omicsbench suite validate --id rnaseq-002 --id sequence-005

To test a differential-expression pipeline across several biological objects, place each result in a directory under its benchmark ID. CSV and TSV are accepted, with optional gzip compression:

results/
  rnaseq-002.tsv.gz
  rnaseq-003.csv

Then run:

omicsbench suite compare results --id rnaseq-002 --id rnaseq-003 --junit reports/comparison.xml

Compare several tools or parameter sets in one matrix:

omicsbench suite matrix \
  --method deseq2=results/deseq2 \
  --method edger=results/edger \
  --json reports/matrix.json \
  --csv reports/matrix.csv \
  --html reports/matrix.html

Use a previously accepted report as a regression baseline:

omicsbench suite regress accepted.json candidate.json --absolute-tolerance 0.01 --junit reports/regression.xml

Omit --id to require results for all 12 comparable RNA-seq objects. A completed suite exits with status 0 when every check passes, status 2 when a benchmark or regression gate fails, and status 1 for invalid input. Existing report files are protected unless --force is supplied. The suite reference describes the file convention and report fields.

Portable bundles

Create a deterministic ZIP containing selected benchmarks, their manifests, declared files and the project licence material:

omicsbench bundle create rna-and-dna.zip --id rnaseq-002 --id sequence-005
omicsbench bundle verify rna-and-dna.zip

Bundle verification checks the complete file inventory, byte counts, SHA-256 digests and embedded dataset manifests without extracting the archive. Recreating the same selection with the same package version produces the same archive bytes. See docs/portable-bundles.md.

Assay profiles

The assay profiles explain what the local tools can check and where a dedicated workflow becomes necessary:

omicsbench assay list
omicsbench assay info whole-genome
omicsbench assay plan whole-genome sample_R1.fastq.gz sample_R2.fastq.gz --adapter AGATCGGAAGAGC

Profiles are available for whole-genome, exome, targeted-panel, bulk RNA-seq, single-cell RNA-seq, ATAC-seq, ChIP-seq and related assays, amplicon sequencing, shotgun metagenomics, long-read DNA and RNA, genome or transcriptome references, RNA FASTA and protein FASTA. A plan contains executable validation and preprocessing commands plus a clear boundary for the downstream tools that still need a reference, database or assay-specific model.

Bundled sequence benchmarks

ID	Content	Format	Records
sequence-001	DNA with IUPAC ambiguity and an exact duplicate	FASTA	4
sequence-002	coding, ambiguous and short RNA records	FASTA	3
sequence-003	standard, ambiguous and terminating protein records	FASTA	3
sequence-004	paired DNA reads with mixed qualities and duplication	FASTQ	4 pairs
sequence-005	paired DNA-seq reads, reference and three known SNVs	FASTQ	20 pairs

These five objects are project-authored synthetic fixtures. Each has an exact file inventory, checksums, format and molecule declarations, expected summary metrics and a deterministic rebuild workflow. sequence-005 also verifies that every truth-set reference allele matches the bundled reference and that every alternate allele is supported by the paired reads. The objects test software behavior and do not represent a biological cohort or sequencing instrument.

Bulk RNA-seq benchmarks

Version 2 retains the complete version 1 biological collection unchanged.

ID	Design	Samples	Pocket genes
rnaseq-002	SLC2A5 knockout in A549 xenografts	10	2,000
rnaseq-003	AtRsgA knockout in Arabidopsis seedlings	6	4,000
rnaseq-004	Klf1 knockout in mouse erythroid tissue	6	4,000
rnaseq-005	Paired prostate tumour and adjacent tissue	28	8,000
rnaseq-006	Arabidopsis infection adjusted for genotype	12	500
rnaseq-007	ELP3 depletion in BT549 cells	6	8,000
rnaseq-008	Duchenne muscular dystrophy myoblasts	9	8,000
rnaseq-009	Arabidopsis genotype adjusted for infection	12	500
rnaseq-010	Infection response in wild-type Arabidopsis	6	500
rnaseq-011	Infection response in gsnor1 Arabidopsis	6	500
rnaseq-012	Dmd-mdx myoblasts against wild type	6	8,000
rnaseq-013	Dmd-mdx-beta-geo myoblasts against wild type	6	8,000

Each biological object includes the source counts for its selected samples, a smaller pocket matrix, ordered sample metadata, the declared design, source attribution, rights evidence, reference details, transformations and quantitative validation. All 12 pockets pass their predeclared DESeq2 fidelity thresholds.

Compare another tool's gene-level effects with a bundled full-source DESeq2 reference:

omicsbench compare rnaseq-002 results.csv

The input must contain unique gene_id and finite log2_fold_change columns. The report includes rank correlation, top-50 overlap, sign agreement, coverage and identifier differences. Status 0 means the declared checks passed, status 2 means a completed comparison missed a scientific threshold and status 1 means the input was invalid.

Collection records

The JSON catalog contains both benchmark families. catalog/collection.tsv lists the biological objects and catalog/sequences.tsv lists the sequence fixtures.

Expression Atlas and BioStudies supplied the biological count matrices. Their bundled material is recorded as CC BY 4.0 with provider attribution. The sequence fixtures and software are project-authored and distributed under Apache-2.0. Project-written descriptive metadata is CC BY 4.0.

The objects are research and software-test material. They are not clinical references and do not replace the complete source studies.

Development checks

python -m unittest discover -s tests -v
python workflows/build_sequence_fixtures.py
python scripts/build_catalog.py
python scripts/audit_collection.py
python scripts/check_repository.py
python scripts/certify_release.py --preflight

The repository checker validates every manifest, file inventory, byte count, SHA-256 digest and expected result. It also rejects undeclared files, schema drift, unsupported metadata and prohibited attribution traces.

Citation

Zenodo releases list Vivaan Patni as the author. GitHub development and commits use the account vxxqv. Cite the exact archived release used in an analysis. The concept DOI 10.5281/zenodo.22551734 resolves to the latest archived version.