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.

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

Quick Start

python

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

python

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

python

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

python

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

4. Recommendation Generation

python

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

bash

# 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