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BytesAgain β€Ί Skills β€Ί Fastqc Report Interpreter
πŸ¦€ ClawHub

Fastqc Report Interpreter

by @aipoch-ai

Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interpret...

Versionv0.1.0
Downloads412
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TERMINAL
clawhub install fastqc-report-interpreter

πŸ“– About This Skill

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

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

    πŸ’‘ Examples

    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}")