Summary
QC results become something you can discuss, not just a folder of HTML reports.
The async pair is what makes it usable on real data: a batch over a whole directory is queued and polled rather than blocking the session, and MultiQC then folds the individual reports into one summary you can actually read across samples.
What it is
A server that runs FastQC and MultiQC over sequencing data: one file, a directory of them, or a combined summary report — with async variants and a job queue for long runs.
What you get
- FastQC on a single FASTQ or FASTA file, returning pass, warn or fail per module
- FastQC across a directory, with a file pattern and a thread count you choose
- A MultiQC report combining the FastQC results from a directory
- Custom contaminants, adapters and limits files passed through to FastQC
- Async variants of all three, for datasets too large to wait on
- Job status and results retrieved by ID, and a queued job cancelled
Requirements
FastQC and MultiQC installed on the same machine — conda is the route the project recommends, with apt or brew plus pip as alternatives. Python, with the package installed into the environment.
Setup effort
One command — pip install git+https://github.com/bio-mcp/bio-mcp-fastqc.git