bio-read-qc-quality-reports
SkillFiles & storageGenerate 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.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the bio-read-qc-quality-reports skill
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/bio-read-qc-quality-reports/SKILL.md and read by ahel’s review.
Version Compatibility
Reference examples tested with: pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Quality Reports
Generate quality reports for FASTQ files using FastQC and aggregate multiple reports with MultiQC.
"Run quality control on FASTQ files" → Generate per-base quality, adapter content, and duplication plots, then aggregate across samples.
- CLI:
fastqc *.fastq.gzthenmultiqc .
FastQC - Single Sample Reports
Basic Usage
# 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 reportsample_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
# 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
# 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
# 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 reportmultiqc_data/- Directory with data tablesmultiqc_fastqc.txt- FastQC metricsmultiqc_general_stats.txt- Summary statisticsmultiqc_sources.txt- Source files used
Extract Data Programmatically
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
# 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
# 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/writing
Signals
- GitHub stars
- 3k
- Forks
- 412
- Last commit
- Jul 2026
ahel review
K1binfo
installs-packages (in usage-guide.md)
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Item type
- skill
- Key
bio-read-qc-quality-reports- Source
- github.com/freedomintelligence/openclaw-medical-skills
github.com/freedomintelligence/openclaw-medical-skills
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