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Scientific-ToolingScientific-Tooling/structured-intelligence/skills/rnaseq-alignment-qc/SKILL.md

rnaseq-alignment-qc

Skill4 starsChanged 12 days agoMITView on GitHub

RNA-seq-specific alignment quality assessment using RSeQC and Qualimap for gene body coverage, strandedness, and rRNA contamination.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 1 is distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run RSeQC infer_experiment.py to determine library strandedness.

    line 28
  2. Very specific:

    Run RSeQC geneBody_coverage.py for 5'-to-3' coverage profile across gene bodies.

    line 29
  3. Very specific:

    Run RSeQC read_distribution.py for read distribution across genomic features (CDS, UTR, intron, intergenic).

    line 30
  4. Very specific:

    Run RSeQC inner_distance.py for insert size distribution (paired-end data).

    line 31
  5. Very specific:

    Run Qualimap rnaseq for comprehensive RNA-seq QC metrics.

    line 32
  6. Distinctive:

    Read distribution table (text)

    line 41

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Skill: RNA-seq Alignment QC

Use When

  • User wants to assess RNA-seq alignment quality beyond basic mapping stats
  • User needs to check strandedness of a library
  • User wants to evaluate gene body coverage (5'-to-3' bias)
  • User wants to measure rRNA contamination rate
  • User needs to verify read distribution across genomic features (CDS, UTR, intron, intergenic)

Inputs

  • Required:
    • BAM file with index (.bam + .bai)
  • Optional:
    • BED file of gene models (for RSeQC; available from UCSC or RSeQC downloads)
    • GTF annotation file (for Qualimap)
    • Output directory (default: ./alignment_qc_output)
    • Species/genome build for rRNA intervals

Workflow

  1. Run RSeQC infer_experiment.py to determine library strandedness.
  2. Run RSeQC geneBody_coverage.py for 5'-to-3' coverage profile across gene bodies.
  3. Run RSeQC read_distribution.py for read distribution across genomic features (CDS, UTR, intron, intergenic).
  4. Run RSeQC inner_distance.py for insert size distribution (paired-end data).
  5. Run Qualimap rnaseq for comprehensive RNA-seq QC metrics.
  6. Calculate rRNA rate from Qualimap output or custom rRNA interval overlap.
  7. Compile summary: strandedness, gene body coverage shape, rRNA rate, feature distribution, junction saturation.
  8. Flag metrics that fall outside recommended thresholds.

Output Contract

  • RSeQC strandedness report (text)
  • Gene body coverage plot (PDF)
  • Read distribution table (text)
  • Inner distance plot (PDF, paired-end only)
  • Qualimap HTML report
  • Compiled QC summary (text)

Limits

  • RSeQC and Qualimap must be installed and available on PATH.
  • BED gene model file is required for most RSeQC modules (downloadable from UCSC or RSeQC site).
  • Qualimap requires Java to be installed.
  • Qualimap memory scales with BAM size (approximately 2-4 GB for human genome BAMs).
  • Common failure cases: missing BED file, BAM without index, Java not available for Qualimap.

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