# Ngs Fastq Qc

> Validate FASTQ inputs, run local FastQC/MultiQC QC, interpret QC signals, and optionally execute fastp or Cutadapt trimming branches without overwriting raw reads.

- Skill: `concertonotes/ngs-fastq-qc` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add concertonotes/ngs-fastq-qc`
- Raw SKILL.md: https://api.skillmd.com/api/skills/concertonotes/ngs-fastq-qc/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ConcertoNotes (https://skillmd.com/u/concertonotes)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/concertonotes/ngs-fastq-qc

---


# 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

```bash
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:

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

Single paired sample:

```bash
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:

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

For explicit adapters:

```bash
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:

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

QC plus trimming:

```bash
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.