Skills fastqc-report-interpreter

Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/fastqc-report-interpreter" ~/.claude/skills/clawdbot-skills-fastqc-report-interpreter && rm -rf "$T"

manifest: skills/aipoch-ai/fastqc-report-interpreter/SKILL.md

source content

FASTQC Report Interpreter

Analyze FASTQC quality control reports for Next-Generation Sequencing (NGS) data to assess data quality and identify issues.

Quick Start

from scripts.fastqc_interpreter import FASTQCInterpreter

interpreter = FASTQCInterpreter()

# Analyze report
analysis = interpreter.analyze("sample_fastqc.html")
print(f"Overall Quality: {analysis.quality_status}")
print(f"Issues Found: {analysis.issues}")

Core Capabilities

1. Quality Metrics Analysis

metrics = interpreter.parse_metrics("fastqc_data.txt")

Key Metrics:

Metric	Good	Warning	Fail
Per base sequence quality	Q > 28	Q 20-28	Q < 20
Per sequence quality scores	Peak at Q30	Peak Q20-30	Peak < Q20
Per base N content	< 5%	5-20%	> 20%
Sequence duplication	< 20%	20-50%	> 50%
Adapter content	< 5%	5-10%	> 10%

2. Issue Diagnosis

issues = interpreter.diagnose_issues(metrics)
for issue in issues:
    print(f"{issue.severity}: {issue.description}")
    print(f"Recommendation: {issue.recommendation}")

Common Issues:

Low Quality at Read Ends

Cause: Phasing effects, reagent depletion
Solution: Trim last 10-20 bases

Adapter Contamination

Cause: Incomplete adapter removal
Solution: Re-run cutadapt/Trimmomatic with stricter parameters

High Duplication

Cause: PCR over-amplification, low input
Solution: Use deduplication; consider library prep optimization

Per Base Sequence Content Bias

Cause: Adapter dimers, non-random priming
Solution: Check for adapter contamination; randomize primers

3. Batch Analysis

batch_results = interpreter.analyze_batch(
    fastqc_files=["sample1_fastqc.html", "sample2_fastqc.html", ...],
    output_summary="batch_summary.csv"
)

4. Recommendation Generation

recommendations = interpreter.get_recommendations(
    analysis,
    application="rna_seq",  # or "dna_seq", "chip_seq"
    quality_threshold="high"
)

Application-Specific Thresholds:

RNA-seq: Acceptable duplication up to 40% (transcript abundance)
DNA-seq: Strict quality requirements (variant calling)
ChIP-seq: Moderate quality, focus on enrichment metrics

CLI Usage

# Analyze single report
python scripts/fastqc_interpreter.py --input sample_fastqc.html

# Batch analysis
python scripts/fastqc_interpreter.py --batch "*fastqc.html" --output report.pdf

# With custom thresholds
python scripts/fastqc_interpreter.py --input fastqc.html --application rna_seq

Output Interpretation

PASS (Green): Proceed with analysis WARNING (Yellow): Review but likely acceptable FAIL (Red): Requires action before downstream analysis

Troubleshooting Guide

See

references/troubleshooting.md

for:

Platform-specific issues (Illumina, PacBio, Oxford Nanopore)
Library prep problem diagnosis
Downstream analysis impact assessment

Skill ID: 205 | Version: 1.0 | License: MIT