CtrlK
BlogDocsLog inGet started
Tessl Logo

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 with Tessl CLI

npx tessl i github:aipoch/medical-research-skills --skill fastqc-report-interpreter
What are skills?

85

Review — 81%

Does it follow best practices?

Validation — 11 / 11 Passed

Validation for skill structure

SKILL.md
Review
Evals

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:

MetricGoodWarningFail
Per base sequence qualityQ > 28Q 20-28Q < 20
Per sequence quality scoresPeak at Q30Peak Q20-30Peak < 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

Repository
aipoch/medical-research-skills
Commit

f11484c

Last updated
Created

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.

Claim skill