Validate FASTQ inputs, run local FastQC/MultiQC QC, interpret QC signals, and optionally execute fastp or Cutadapt trimming branches without overwriting raw reads.
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---
name: x-openai-ngs-fastq-qc-d23af8a
description: Validate FASTQ inputs, run local FastQC/MultiQC QC, interpret QC signals, and optionally execute fastp or Cutadapt trimming branches without overwriting raw reads.
---
### 导出副本边界与技能别名
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## 跨宿主使用约定
本副本供支持 Agent Skills 的客户端使用。先检查当前宿主实际提供的工具、权限和已连接服务,再执行正文工作流。原文中的宿主工具标识是能力参考;只有实际发现该工具时才可调用,其他宿主应使用经核验的等价 MCP、CLI 或 API。没有等价能力的步骤必须报告不可用,不能虚构调用或完成状态。
技能资源以本技能目录为基准;输出写入当前项目中用户指定的位置。通过技能名称或自然语言触发,具体快捷命令由宿主决定。涉及真实产品、模型 API、协议字段和官方网址的名称保持原意。授权、审批、隐私和工具范围限制在迁移后仍然有效;不得为兼容而扩大权限。具体依赖与迁移说明见同目录 `PORTABILITY.md`。
# 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.