# 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

# 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"

# 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

# 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*'

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')

# All FASTQ files in parallel
fastqc -t 8 -o qc_reports/ raw_data/*.fastq.gz

# Then aggregate
multiqc qc_reports/ -o multiqc_output/

# 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/

Custom_Adapter_Name    ACGTACGTACGT

# Warn if mean quality below 25
quality_sequence    warn    25
quality_sequence    error   20