fastqc-report-interpreter

name	fastqc-report-interpreter
description	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.
allowed-tools	Read Write Bash Edit
license	MIT
metadata	{"skill-author":"AIPOCH","version":"1.0"}

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

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