bio-read-qc-quality-reports
Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.
Best use case
bio-read-qc-quality-reports is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.
Teams using bio-read-qc-quality-reports should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-read-qc-quality-reports/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-read-qc-quality-reports Compares
| Feature / Agent | bio-read-qc-quality-reports | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Quality Reports
Generate quality reports for FASTQ files using FastQC and aggregate multiple reports with MultiQC.
## FastQC - Single Sample Reports
### Basic Usage
```bash
# Single file
fastqc sample.fastq.gz
# Multiple files
fastqc *.fastq.gz
# Specify output directory
fastqc -o qc_reports/ sample_R1.fastq.gz sample_R2.fastq.gz
# Set threads
fastqc -t 4 *.fastq.gz
```
### Output Files
FastQC produces two files per input:
- `sample_fastqc.html` - Interactive HTML report
- `sample_fastqc.zip` - Data files and images
### Key Modules
| Module | What It Shows | Warning Signs |
|--------|---------------|---------------|
| Per base sequence quality | Quality scores across read | Drop below Q20 at 3' end |
| Per sequence quality | Quality score distribution | Bimodal distribution |
| Per base sequence content | Nucleotide composition | Imbalance at start (normal) |
| Per sequence GC content | GC distribution | Secondary peak (contamination) |
| Per base N content | Unknown bases | High N content |
| Sequence length distribution | Read lengths | Unexpected variation |
| Sequence duplication | Duplicate reads | High duplication (PCR) |
| Overrepresented sequences | Common sequences | Adapter contamination |
| Adapter content | Adapter sequences | Visible adapter curves |
### Extract Data from ZIP
```bash
# Unzip to access raw data
unzip sample_fastqc.zip
# View summary
cat sample_fastqc/summary.txt
# Get per-base quality
cat sample_fastqc/fastqc_data.txt | grep -A 50 ">>Per base sequence quality"
```
## MultiQC - Aggregate Reports
### Basic Usage
```bash
# Aggregate all FastQC reports in current directory
multiqc .
# Specify input and output
multiqc qc_reports/ -o multiqc_output/
# Custom report name
multiqc . -n my_project_qc
# Force overwrite
multiqc . -f
```
### Common Options
```bash
# Flat directory (no sample subdirs)
multiqc --flat .
# Export data as TSV
multiqc . --export
# Only specific modules
multiqc . -m fastqc
# Exclude patterns
multiqc . --ignore '*_trimmed*'
# Include patterns
multiqc . --ignore-samples '*negative*'
```
### Output Files
- `multiqc_report.html` - Interactive HTML report
- `multiqc_data/` - Directory with data tables
- `multiqc_fastqc.txt` - FastQC metrics
- `multiqc_general_stats.txt` - Summary statistics
- `multiqc_sources.txt` - Source files used
### Extract Data Programmatically
```python
import pandas as pd
general_stats = pd.read_csv('multiqc_data/multiqc_general_stats.txt', sep='\t')
print(general_stats.columns)
fastqc_data = pd.read_csv('multiqc_data/multiqc_fastqc.txt', sep='\t')
```
## Batch Processing
### Process Multiple Samples
```bash
# All FASTQ files in parallel
fastqc -t 8 -o qc_reports/ raw_data/*.fastq.gz
# Then aggregate
multiqc qc_reports/ -o multiqc_output/
```
### Before and After Trimming
```bash
# Create separate directories
mkdir -p qc_reports/raw qc_reports/trimmed
# QC raw reads
fastqc -o qc_reports/raw/ raw_data/*.fastq.gz
# After trimming (using fastp, cutadapt, etc.)
fastqc -o qc_reports/trimmed/ trimmed_data/*.fastq.gz
# Compare with MultiQC
multiqc qc_reports/ -o qc_comparison/
```
## Interpretation Guide
### Quality Scores
| Phred Score | Error Rate | Interpretation |
|-------------|------------|----------------|
| Q40 | 0.0001 | Excellent |
| Q30 | 0.001 | Good (Illumina target) |
| Q20 | 0.01 | Acceptable |
| Q10 | 0.1 | Poor |
### Common Issues
| Issue | Likely Cause | Action |
|-------|--------------|--------|
| Low quality at 3' end | Normal degradation | Trim 3' end |
| Adapter contamination | Short inserts | Trim adapters |
| GC bias | Library prep | Consider correction |
| High duplication | Low complexity, PCR | Mark/remove duplicates |
| Overrepresented seqs | Adapters, primers | Check sequences |
## Configuration
### Custom Adapters
Create `~/.fastqc/Configuration/adapter_list.txt`:
```
Custom_Adapter_Name ACGTACGTACGT
```
### Custom Limits
Create `~/.fastqc/Configuration/limits.txt` to customize thresholds:
```
# Warn if mean quality below 25
quality_sequence warn 25
quality_sequence error 20
```
## Related Skills
- adapter-trimming - Remove adapters detected by FastQC
- fastp-workflow - All-in-one QC and trimming
- sequence-io/read-sequences - FASTQ file reading/writingRelated Skills
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