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Installation

Installing gb-qc

pip install genomic-benchmarks-qc

That is everything evaluate-classes needs. Python 3.12 or newer; tested on 3.12, 3.13 and 3.14. The scientific stack it depends on — NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, Biopython and Typer — comes in with it.

Check it landed:

gb-qc --help

You should see the two commands, evaluate-classes and evaluate-splits.

MMseqs2, for the leakage check

evaluate-splits measures how much of your test set already appears in your training set, and it does that with a similarity search rather than string matching. That search is MMseqs2, which is a separate binary: it is not a Python package and pip will not bring it in. It has to be on your PATH under the name mmseqs.

Use a precompiled binary. MMseqs2 ships static builds for Linux and macOS, and they are self-contained — pick the one for your machine from the installation guide, unpack it, and put its bin/ directory on your PATH.

Then confirm it:

mmseqs version

If that prints a version, evaluate-splits will find it. If it prints command not found, see the troubleshooting entry.

evaluate-classes does not need MMseqs2. If the class checks are all you want, skip this section — nothing else in the tool touches it.

Installing gb-qc from source

Back to the Python package — MMseqs2 is done with. To get the current main rather than the last release:

pip install git+https://github.com/genomic-benchmarks/genomic-benchmarks-qc.git

For a checkout you intend to edit, the repository ships a conda environment with the test and lint tooling in it:

git clone https://github.com/genomic-benchmarks/genomic-benchmarks-qc.git
cd genomic-benchmarks-qc
conda env create -f dev-requirements.yml
conda activate gb-qc-dev
pip install -e '.[develop]'

CONTRIBUTING.md covers the rest — running the tests, the lint rules, and how the example reports are built.

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