Fastq Analysis Pipeline

Overview

OmicVerse provides a complete FASTQ-to-count-matrix pipeline via the ov.alignment module. This skill covers:

• SRA data acquisition: prefetch and fqdump (fasterq-dump wrapper)
• Quality control: fastp for adapter trimming and QC reports
• RNA-seq alignment: STAR aligner with auto-index building
• Gene quantification: featureCount (subread featureCounts wrapper)
• Single-cell path: ref and count via kb-python (kallisto/bustools)
• Parallel SRA download: parallel_fastq_dump

All functions share a common CLI infrastructure (_cli_utils.py) that handles tool resolution, auto-installation via conda/mamba, parallel execution, and streaming output.

Instructions

1. Environment setup

• Bioinformatics tools are resolved automatically from PATH or the active conda environment.
• If auto_install=True (default), missing tools are installed via mamba/conda on demand.
• Supported tools: prefetch, vdb-validate, fasterq-dump, fastp, STAR, samtools, featureCounts, pigz, gzip.
• For the single-cell path, ensure kb-python is installed: pip install kb-python.

1. SRA data download (ov.alignment.prefetch + ov.alignment.fqdump)

• Use prefetch first for reliable downloads with integrity validation (vdb-validate).
• Then convert to FASTQ with fqdump. It auto-detects single-end vs paired-end.
• fqdump can also work directly from SRR accessions without prefetch.
• Both support retry with exponential backoff for network errors.

python import omicverse as ov

# Step 1: Prefetch SRA files (optional but recommended) pre = ov.alignment.prefetch('SRR1234567', 'SRR1234568'], output_dir='prefetch', jobs=4)

# Step 2: Convert to FASTQ fq = ov.alignment.fqdump('SRR1234567', 'SRR1234568'], output_dir='fastq', sra_dir='prefetch', gzip=True, threads=8, jobs=4)

1. FASTQ quality control (ov.alignment.fastp)

• Runs fastp for adapter trimming, quality filtering, and QC reporting.
• Supports single-end and paired-end reads.
• Produces per-sample JSON and HTML QC reports.
• Sample format: tuple of (sample_name, fq1_path, fq2_path_or_None).

python samples = [ ('S1', 'fastq/SRR1234567/SRR1234567_1.fastq.gz', 'fastq/SRR1234567/SRR1234567_2.fastq.gz'), ('S2', 'fastq/SRR1234568/SRR1234568_1.fastq.gz', 'fastq/SRR1234568/SRR1234568_2.fastq.gz'), ] clean = ov.alignment.fastp(samples, output_dir='fastp', threads=8, jobs=2)

1. STAR alignment (ov.alignment.STAR)

• Aligns FASTQ reads using the STAR aligner.
• Auto-index building: set auto_index=True (default) with genome_fasta_files and gtf to build index automatically if missing.
• Produces coordinate-sorted BAM files.
• Handles gzip-compressed FASTQs automatically (uses pigz/gzip/zcat).
• Use strict=False (default) for graceful error handling per sample.

python # Prepare samples from fastp output star_samples = [ ('S1', 'fastp/S1/S1_clean_1.fastq.gz', 'fastp/S1/S1_clean_2.fastq.gz'), ('S2', 'fastp/S2/S2_clean_1.fastq.gz', 'fastp/S2/S2_clean_2.fastq.gz'), ] bams = ov.alignment.STAR( star_samples, genome_dir='star_index', output_dir='star_out', gtf='genes.gtf', genome_fasta_files=['genome.fa'], threads=8, memory='50G', )

1. Gene quantification (ov.alignment.featureCount)

• Counts aligned reads per gene using featureCounts (subread).
• Auto-detects paired-end from BAM headers (via pysam or samtools).
• auto_fix=True (default) retries with corrected paired-end flag on error.
• gene_mapping=True maps gene_id to gene_name from the GTF.
• merge_matrix=True produces a combined count matrix across all samples.

python bam_items = [ ('S1', 'star_out/S1/Aligned.sortedByCoord.out.bam'), ('S2', 'star_out/S2/Aligned.sortedByCoord.out.bam'), ] counts = ov.alignment.featureCount( bam_items, gtf='genes.gtf', output_dir='counts', gene_mapping=True, merge_matrix=True, threads=8, ) # counts is a pandas DataFrame (gene_id x samples)

1. Single-cell path (ov.alignment.ref + ov.alignment.count)

• Uses kb-python (kallisto + bustools) for single-cell RNA-seq quantification.
• ref() builds a kallisto index and transcript-to-gene mapping.
• count() quantifies single-cell data with barcode/UMI handling.
• Supports technologies: 10XV2, 10XV3, BULK, and custom.
• Output formats: h5ad, loom, cellranger MTX.

python # Build reference index ref_result = ov.alignment.ref( index_path='kb_ref/index.idx', t2g_path='kb_ref/t2g.txt', fasta_paths='genome.fa'], gtf_paths='genes.gtf'], threads=8, )

# Quantify 10x v3 data count_result = ov.alignment.count( index_path='kb_ref/index.idx', t2g_path='kb_ref/t2g.txt', technology='10XV3', fastq_paths='sample_R1.fastq.gz', 'sample_R2.fastq.gz'], output_path='kb_out', h5ad=True, filter_barcodes=True, threads=8, )

1. Wiring fastp output into STAR input

• fastp output is a list of dicts with keys: sample, clean1, clean2, json, html.
• Convert to STAR sample tuples:

python star_samples = [ (r['sample'], r['clean1'], r['clean2'] if r['clean2'] else None) for r in (clean if isinstance(clean, list) else [clean]) ]

1. Wiring STAR output into featureCount input

• STAR output is a list of dicts with keys: sample, bam (or error).
• Convert to featureCount items:

python bam_items = [ (r['sample'], r['bam']) for r in (bams if isinstance(bams, list) else [bams]) if 'bam' in r ]

1. Skipping completed steps

• All functions check for existing outputs and skip if overwrite=False (default).
• Set overwrite=True to force re-execution.

1. Troubleshooting

• If a tool is not found, check auto_install=True and that conda/mamba is accessible.
• For STAR index errors, ensure genome_fasta_files points to uncompressed or gzip FASTA files.
• For featureCounts paired-end detection errors, auto_fix=True handles most cases automatically.
• GTF files can be gzip-compressed; they are auto-decompressed as needed.

Critical API Reference

Sample Format Convention

All alignment functions use a consistent sample tuple format:

• FASTQ samples: (sample_name, fq1_path, fq2_path_or_None)
• BAM items: (sample_name, bam_path) or (sample_name, bam_path, is_paired_bool)
• Single samples can be passed as a single tuple; multiple as a list of tuples.
• When a single tuple is passed, the return value is a single dict; for a list, a list of dicts.

Auto-installation

python
# All functions support these parameters:
auto_install=True   # Auto-install missing tools via conda/mamba
overwrite=False     # Skip if outputs already exist
threads=8           # Per-tool thread count
jobs=None           # Concurrent job count (auto-detected from CPU count)

Examples

• Bulk RNA-seq from SRA: prefetch -> fqdump -> fastp -> STAR -> featureCount -> pandas DataFrame
• Single-cell 10x v3: ref -> count with technology='10XV3' -> h5ad AnnData
• Local FASTQ files: Skip download steps, start directly with fastp -> STAR -> featureCount

References

• See reference.md for copy-paste-ready code templates.