ngs-fastq-qc

name	ngs-fastq-qc
description	Validate FASTQ inputs, run local FastQC/MultiQC QC, interpret QC signals, and optionally execute fastp or Cutadapt trimming branches without overwriting raw reads.

Prerequisite: the plugins/ngs-analysis/ plugin root (its scripts/*.py and references/*.json) is not vendored in this flat vault. Install the ngs-analysis plugin from openai/plugins at the repo root, or every python plugins/ngs-analysis/... command below fails with No such file or directory.

FASTQ QC

Use this skill for QC-only, trimming-first, or FASTQ quality interpretation workflows. This skill can execute the plugin-owned local FastQ QC runner when the user approves a local run. It should decide whether trimming or additional investigation is warranted; it should not blindly trim by default.

Essential Inputs

Confirm:

• FASTQ paths and pairing convention
• whether output should be QC-only or trimmed FASTQs
• known adapter or primer sequences
• organism if contamination screening or host depletion is requested
• output directory
• whether FASTQs are raw, demultiplexed, previously trimmed, or downloaded from an archive
• whether downstream analysis expects original read lengths, UMIs, or inline barcodes

Public Tools

Default tool set:

• FastQC for raw read QC
• MultiQC for project-level summary
• fastp for all-in-one QC/trimming when acceptable
• Cutadapt when primer/adapter handling needs explicit sequences
• seqkit for quick counts, stats, and subsampling

Preflight

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline fastq_qc --emit-install-plan

Local Execution

Use the plugin-owned runner for local artifact-producing FASTQ QC:

python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample-sheet samplesheet.csv \
  --execute

Single paired sample:

python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample sampleA \
  --r1 sampleA_R1.fastq.gz \
  --r2 sampleA_R2.fastq.gz \
  --execute

Optional trimming branch:

python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample-sheet samplesheet.csv \
  --trim-mode fastp \
  --execute

For explicit adapters:

python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample-sheet samplesheet.csv \
  --trim-mode cutadapt \
  --adapter-r1 AGATCGGAAGAGC \
  --adapter-r2 AGATCGGAAGAGC \
  --execute

The runner performs pre-execution validation before Snakemake execution. It writes a timestamped run directory with run_manifest.json, config.json, validation/, workflow/Snakefile, logs, artifact_index.json, summary.md, FastQC/MultiQC outputs, and qc_interpretation.json after successful execution.

Interpretation Rules

Inspect raw QC before recommending trimming:

• Per-base quality drop at the read end: consider quality trimming, but preserve enough length for alignment or amplicon merging.
• Adapter or primer signal: use cutadapt when explicit sequences matter; use fastp only when automatic handling is acceptable.
• Poly-G or patterned-flowcell artifacts: handle with a tool that explicitly supports the artifact and report the assumption.
• Overrepresented sequences: classify adapters, primers, rRNA, PhiX, host contamination, or true biology before filtering.
• Per-tile failures or severe quality shifts: flag possible run-level issues and avoid treating them as ordinary adapter contamination.
• High duplication: interpret by assay; it may be expected for amplicons, targeted panels, or low-input libraries.
• Pairing issues: verify R1/R2 file counts and read-name pairing before any downstream workflow.

Do not overwrite input FASTQs. Preserve the raw QC reports even when trimmed FASTQs are created.

Kickoff Pattern

QC-only:

mkdir -p results/fastqc results/multiqc
fastqc -t 4 -o results/fastqc *.fastq.gz
multiqc results/fastqc -o results/multiqc

QC plus trimming:

fastp \
  -i sample_R1.fastq.gz \
  -I sample_R2.fastq.gz \
  -o results/trimmed/sample_R1.fastq.gz \
  -O results/trimmed/sample_R2.fastq.gz \
  --html results/fastp/sample.html \
  --json results/fastp/sample.json
multiqc results -o results/multiqc

Output Review

Return a short QC interpretation with:

1. sample/read-pair inventory
2. QC modules that look normal
3. QC modules that require action or user confirmation
4. trimming or no-trimming recommendation with rationale
5. downstream caveats such as short reads, contaminated libraries, or failed pairs

When using the local runner, ground the response in the generated qc_interpretation.json, summary.md, and MultiQC report instead of relying only on expected artifacts.