fastq-analysis-pipeline（fastq 分析管道）

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) 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)

- 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.

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

- 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).

1. STAR alignment (ov.alignment.STAR) # 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',)

- 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.

1. Gene quantification (ov.alignment.featureCount) 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)

- 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.

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

- 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.

1. Wiring fastp output into STAR input star_samples = [(r['sample'], r['clean1'], r['clean2'] if r['clean2'] else None) for r in (clean if isinstance(clean, list) else [clean])]

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

1. Wiring STAR output into featureCount input bam_items = [(r['sample'], r['bam']) for r in (bams if isinstance(bams, list) else [bams]) if 'bam' in r]

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

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

# 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.