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

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

Common Issues

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