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Scientific-ToolingScientific-Tooling/structured-intelligence/skills/ngs-quality-control/SKILL.md

ngs-quality-control

Skill4 starsChanged 12 days agoMITView on GitHub

Raw read quality assessment with FastQC and multi-sample report aggregation with MultiQC.

1 other project uses a version of this.

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The file

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2 rules. About 0 are common, 1 is distinctive, and 1 is 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.

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    Very specific

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

    Run FastQC on each FASTQ file, supporting parallel execution via --threads.

    line 28
  2. Distinctive:

    Run MultiQC to aggregate individual FastQC reports into a single HTML summary.

    line 30

≈ “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.

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Files
SKILL.md

Skill: ngs-quality-control

Use When

  • The user wants to assess raw FASTQ read quality before downstream analysis.
  • The user needs per-sample QC reports generated with FastQC.
  • The user wants to aggregate QC results across multiple samples into a single summary with MultiQC.
  • The user wants to check for adapter contamination, per-base quality drop-off, GC bias, or duplication levels before proceeding to trimming or alignment.

Inputs

  • Required:
    • FASTQ file(s) or a directory containing FASTQ files (.fastq, .fq, .fastq.gz, .fq.gz).
  • Optional:
    • --outdir DIR — Output directory for reports (default: fastqc_results).
    • --threads N — Number of parallel threads for FastQC (default: 4).
    • --multiqc-config FILE — Custom MultiQC configuration file for report customization.
    • Additional FastQC modules to disable (pass via extra flags).

Workflow

  1. Validate that input FASTQ files exist and are readable.
  2. Run FastQC on each FASTQ file, supporting parallel execution via --threads.
  3. Collect all FastQC output directories (HTML reports and ZIP archives).
  4. Run MultiQC to aggregate individual FastQC reports into a single HTML summary.
  5. Parse key metrics from the results: per-base sequence quality, adapter content, sequence duplication levels, and per-sequence GC distribution.
  6. Flag samples that fail critical quality thresholds (refer to knowledge/sources/genomics/quality-thresholds.md for threshold definitions).
  7. Report summary statistics and the locations of the generated HTML reports.

Output Contract

  • FastQC HTML report — One per input FASTQ file (<sample>_fastqc.html).
  • FastQC ZIP archive — One per input FASTQ file (<sample>_fastqc.zip), containing raw data and images.
  • MultiQC HTML report — Aggregated summary at <outdir>/multiqc/multiqc_report.html.
  • Summary table — Pass/Warn/Fail status per FastQC module per sample.
  • Flagged samples list — Samples failing critical thresholds, with the specific modules that triggered the flag.

Limits

  • FastQC and MultiQC must be installed and available on $PATH.
  • FastQC requires approximately 250 MB of memory per thread.
  • This skill does not perform adapter trimming or quality filtering; use ngs-read-preprocessing for that purpose.
  • MultiQC aggregation requires that all FastQC outputs are in a single directory tree.
  • Very large cohorts (hundreds of samples) may require increased memory for MultiQC.
  • Common failure cases:
    • Input files not in FASTQ format or corrupted gzip archives.
    • Insufficient disk space for FastQC output (HTML + ZIP per sample).
    • MultiQC version mismatch causing module parsing errors on newer FastQC output.

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