FastQC

FastQC is a quality control tool for high throughput sequence data. It runs a modular set of analyses to flag problems before downstream analysis. In non-interactive mode it processes all specified files and produces an HTML report for each.

Key features:

• Import data from BAM, SAM or FastQ files
• Summary graphs and tables of quality control metrics
• Export reports in HTML format

Usage

1. Prepare data

Upload your FASTQ files:

# Create input directory
mkdir -p input output

# Upload your sequencing files
dxflow artifact upload /local/sample_R1.fastq.gz input/
dxflow artifact upload /local/sample_R2.fastq.gz input/

2. Deploy

# Deploy FastQC workflow
dxflow workflow create --identity fastqc-analysis hub://fastqc

3. Start (with optional tuning)

The step reads three env vars: INPUT (the input glob — point it at .fastq.gz, .fastq, .bam, or .sam), THREADS (how many files FastQC processes in parallel — set 1 for serial), and EXTRA (any additional fastqc flags from the Options tables, e.g. --extract, --format bam, --adapters …). Tune them per run with --override — no need to edit the workflow:

# Start with defaults (all *.fastq.gz, THREADS=4, no extra flags)
dxflow workflow start fastqc-analysis

# Or override at start — e.g. process BAM files with extra flags
dxflow workflow start fastqc-analysis \
    --override env.job.INPUT=/data/input/*.bam \
    --override env.job.THREADS=8 \
    --override env.job.EXTRA=--extract

4. Monitor

# View logs
dxflow workflow logs fastqc-analysis

# Check status
dxflow workflow list

5. Retrieve results

# Download HTML reports
dxflow artifact download output/ /local/fastqc-results/

Configuration

name: fastqc
tags:
    - genomics
steps:
    - name: job
      runtime: docker
      mode: parallel
      image: ghcr.io/dxflow-ai/fastqc:latest
      volumes:
          - name: input
            host: ./input
            container: /data/input
            mode: ro
          - name: output
            host: ./output
            container: /data/output
      env:
          - INPUT=/data/input/*.fastq.gz
          - THREADS=4
          - EXTRA=
      resources:
          cpu: "4"
          memory: 4G

[volume]
job.input = ./input
job.output = ./output

[env]
job.INPUT = /data/input/*.fastq.gz
job.THREADS = 4
job.EXTRA =

[resource]
job.cpu = 4
job.memory = 4G

{
    "arch": ["amd64"],
    "image": "ghcr.io/dxflow-ai/fastqc:latest",
    "version": "0.12.1",
    "minimum": {
        "cpu": 2,
        "memory": "2G",
        "storage": "10G"
    }
}

Output files

One pair per input file:

• *_fastqc.html - HTML report with all graphs and tables
• *_fastqc.zip - ZIP archive of the detailed data files, including summary.txt (pass/warn/fail per module) and fastqc_data.txt (raw data); pass --extract to also unpack these to disk

Quality metrics

Basic Statistics

• File name, type, encoding
• Total sequences, filtered sequences
• Sequence length, %GC content

Per Base Sequence Quality

• Quality score distribution across all bases; identifies low quality regions
• PASS: Median quality ≥ 25
• WARN: Lower quartile < 10 or median < 25
• FAIL: Lower quartile < 5 or median < 20

Per Sequence Quality Scores

• Distribution of quality scores across all sequences
• PASS: Most sequences have quality > 27
• WARN: Peak quality < 27
• FAIL: Peak quality < 20

Sequence Duplication Levels

• Degree of duplication in the library; high duplication may indicate PCR amplification issues

Adapter Content

• Presence of adapter sequences; important for trimming decisions

Interpreting results

Good quality data

• Per base quality scores mostly in green zone (>28)
• Even GC content distribution
• Low duplication levels
• No adapter contamination

Issues to watch for

• Declining quality at 3' end → Consider trimming
• Unusual GC content → Possible contamination
• High duplication → Library complexity issues
• Adapter content → Trimming required
• Overrepresented sequences → Possible contamination

Next steps

1. If quality is good: Proceed to alignment/assembly
2. If trimming needed: Use Trimmomatic or fastp
3. If contamination found: Filter/remove contaminant sequences
4. If adapter present: Perform adapter trimming

Options

Basic options

Advanced options

References

• Documentation: FastQC Documentation
• Source Code: GitHub Repository
• Tutorial: FastQC Tutorial
• Video Guide: FastQC Usage Guide

If you use FastQC in your research, please cite:

Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data.
Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/