
Bio Read Qc Quality Reports
- 3 installs
- 1.1k repo stars
- Updated July 25, 2026
- gptomics/bioskills
Generate and aggregate FASTQ quality reports using FastQC and MultiQC to assess raw sequencing data.
About
Generates and interprets FASTQ quality reports using FastQC and aggregates them with MultiQC. Developers use it for initial QC on raw sequencing data or to validate preprocessing results.
- FastQC per-base quality, adapter, GC, and duplication metrics
- Aggregate multiple reports with MultiQC
Bio Read Qc Quality Reports by the numbers
- 3 all-time installs (skills.sh)
- Ranked #1,661 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/gptomics/bioskills --skill bio-read-qc-quality-reportsAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 3 |
|---|---|
| repo stars | ★ 1.1k |
| Last updated | July 25, 2026 |
| Repository | gptomics/bioskills ↗ |
What it does
Generate and aggregate FASTQ quality reports using FastQC and MultiQC to assess raw sequencing data.
Files
Quality Reports
Generate quality reports for FASTQ files using FastQC and aggregate multiple reports with 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.gzOutput 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 . -fCommon 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 ACGTACGTACGTCustom Limits
Create ~/.fastqc/Configuration/limits.txt to customize thresholds:
# Warn if mean quality below 25
quality_sequence warn 25
quality_sequence error 20Related Skills
- adapter-trimming - Remove adapters detected by FastQC
- fastp-workflow - All-in-one QC and trimming
- sequence-io - FASTQ file reading/writing