geniml

Geniml

Use Geniml for machine learning and statistical workflows over genomic interval sets. Treat coordinates, assemblies, token vocabularies, model artifacts, and sample grouping as explicit contracts. The bundled scripts validate or plan; they do not import Geniml, contact services, deserialize models, or execute training.

Bash is declared only for explicit, user-approved uv, Python, Geniml, Gtars, Git, and native CLI commands shown in this guide; bundled Python helpers do not spawn subprocesses. Example paths under data/, refs/, work/, and models/ are user-provided project placeholders, not missing bundled files.

Verified release snapshot

• Latest stable PyPI release on 2026-07-23: geniml==0.8.4 (2026-01-14).
• PyPI does not declare Requires-Python; its classifiers list Python 3.10-3.14. Prefer Python 3.11 or 3.12 where all native/ML wheels resolve.
• geniml==0.8.4 accepts gtars>=0.2.5; the verified base smoke used current gtars==0.9.2 (2026-06-17, Python >=3.10).
• Extras are ml and test. The base install omits Torch, Gensim, Scanpy, Hugging Face Hub, pyBigWig, and HMM dependencies.
• Upstream documentation contains stale examples. Release source and installed --help output take precedence where they conflict.

Install reproducibly

Use a project environment and commit its generated lockfile:

uv venv --python 3.12
uv pip install "geniml==0.8.4" "gtars==0.9.2"

For Region2Vec, scEmbed, evaluation, or universe methods needing ML libraries:

uv pip install "geniml[ml]==0.8.4" "gtars==0.9.2"

For a durable project, prefer:

uv add "geniml[ml]==0.8.4" "gtars==0.9.2"
uv lock

Do not install an unpinned Git branch. Record Python, OS/architecture, the resolved lockfile, and the PyPI artifact digest. Geniml itself is BSD-2-Clause; the MIT frontmatter value licenses this skill's content.

Start with the safety gate

Before importing Geniml or running an external binary:

1. Work only with explicit local regular files. Reject URLs, FIFOs, devices, and symlinks unless the user deliberately changes that policy.
2. Validate BED structure and the declared assembly against a trusted local chromosome-sizes file.
3. Bound file count, bytes, rows, workers, epochs, and output size.
4. Separate train/validation/test by patient, donor, biological replicate, or other independent unit—not by BED row or cell alone.
5. Inventory and checksum the universe, tokenizer, model, config, inputs, metadata manifest, and native binaries.
6. Obtain explicit approval before any BEDbase or Hugging Face download. Never infer approval from a model ID or BEDbase identifier.
7. Keep logs aggregate and bounded. BED filenames, sample IDs, phenotypes, labels, barcodes, and genomic intervals may be sensitive.