Bio Read Qc Quality Reports

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bio-read-qc-quality-reports

Bio Read Qc Quality Reports is an Agent Skill for documents published in the FreedomIntelligence/OpenClaw-Medical-Skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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.

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QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

Open your project in Codex.
Copy and paste the prompt below.
Review files and risks before approving any action.

Prompt to paste

Inspect the Agent Skill at https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-read-qc-quality-reports/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/bio-read-qc-quality-reports, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Version Compatibility

Reference examples tested with: pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
CLI: <tool> --version then <tool> --help to 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.gz then multiqc .

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 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

# 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 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

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