Geniml

Geniml: Genomic Interval Machine Learning

Overview

Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.

Installation

Install geniml using uv:

uv uv pip install geniml

For ML dependencies (PyTorch, etc.):

uv uv pip install 'geniml[ml]'

Development version from GitHub:

uv uv pip install git+https://github.com/databio/geniml.git

Core Capabilities

Geniml provides five primary capabilities, each detailed in dedicated reference files:

1. Region2Vec: Genomic Region Embeddings

Train unsupervised embeddings of genomic regions using word2vec-style learning.

Use for: Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.

Workflow:

1. Tokenize BED files using a universe reference
2. Train Region2Vec model on tokens
3. Generate embeddings for regions

Reference: See references/region2vec.md for detailed workflow, parameters, and examples.