Fastqc Report Interpreter

面向 NGS 测序数据的 FASTQC 报告解读 Skill：解析碱基质量、N 含量、重复率、接头污染等指标，判定 PASS/WARNING/FAIL，按 RNA-seq、DNA-seq、ChIP-seq 给出差异化阈值与处理建议，支持单文件与批量分析与汇总输出。

aipoch-ai

热度 79.64

最近更新 2026-05-11 17:34:16

分类：数据依赖：代码执行 / 读写文件应用场景：数据处理 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add fastqc-report-interpreter

Metadata

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