🧬 Biotech & Pharma · Bioinformatics

Quality-control sequencing data before analysis

Read-level QC, adapter trimming, contamination screens, and reproducible QC reporting.

intermediate~45 minBioinformaticiansLab ScientistsRegulatory Affairs

Steps

1Run read-level QC: per-base quality, GC bias, duplication profiles
2Trim adapters/low-quality tails with documented parameter versions
3Screen contamination against expected organism panels
4Verify reference genome build matches annotation versions exactly
5Emit multi-sample QC summary tables for batch comparison
6Pin tool versions in a workflow manager (Snakemake/Nextflow) for reruns

Common Pitfalls

▲Mixed genome builds between alignment and annotation
▲Batch effects mistaken for biological signal

Commands

Install with skills CLI

$ npx skills add aniruddhaadak80/skills --skill bioinformatics-seq-qc-pipeline

Install globally

$ npx skills add aniruddhaadak80/skills --skill bioinformatics-seq-qc-pipeline -g

Tags

#sequencing#qc#pipelines#ngs#biotech-pharma#bioinformatics

Install this skill

$ npx skills add aniruddhaadak80/skills --skill bioinformatics-seq-qc-pipeline -a opencode

SKILL.md

# Quality-control sequencing data before analysis

> Read-level QC, adapter trimming, contamination screens, and reproducible QC reporting.

## When to Use This Skill

Read-level QC, adapter trimming, contamination screens, and reproducible QC reporting.

## Steps

1. Run read-level QC: per-base quality, GC bias, duplication profiles
2. Trim adapters/low-quality tails with documented parameter versions
3. Screen contamination against expected organism panels
4. Verify reference genome build matches annotation versions exactly
5. Emit multi-sample QC summary tables for batch comparison
6. Pin tool versions in a workflow manager (Snakemake/Nextflow) for reruns

## Common Pitfalls

- Mixed genome builds between alignment and annotation
- Batch effects mistaken for biological signal

View source on GitHub ↗

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