Geniml: Genomic Interval Machine Learning

Geniml: Genomic Interval Machine Learning

Overview

Geniml is a Python library that bridges genomic interval biology and machine learning. It provides region2vec for learning dense vector representations of genomic regions from BED files, BEDSpace for nearest-neighbor search in embedding space, dataset classes for ML-ready genomic interval loading, and evaluation utilities for embedding quality. Geniml is designed for researchers who want to apply modern ML techniques to chromatin accessibility, histone modification, or other region-based genomic data.

When to Use

• Learn dense embeddings of genomic regions from a collection of BED files to enable ML-based analysis (region2vec)
• Cluster chromatin accessibility peaks or histone modification sites by embedding similarity
• Search for genomic regions similar to a query region using approximate nearest-neighbor search (BEDSpace)
• Build training datasets for ML models from BED-format genomic intervals with a PyTorch-compatible interface
• Compare embedding quality across training runs or datasets using quantitative metrics
• Integrate genomic region representations into custom neural network architectures
• For basic BED file parsing and set operations without ML, use gtars or pysam-genomic-files instead

Prerequisites

• Python packages: geniml, torch, numpy, pandas, anndata
• Data requirements: BED files (minimum 3 columns: chr, start, end); optionally a pre-built universe file
• Environment: Python 3.8+; GPU optional but recommended for region2vec training on large datasets

pip install geniml torch numpy pandas anndata

Quick Start

from geniml.region2vec import Region2VecExModel
from geniml.io import RegionSet

# Load a collection of BED files and train region2vec embeddings
region_sets = [RegionSet("sample1.bed"), RegionSet("sample2.bed"), RegionSet("sample3.bed")]
model = Region2VecExModel("path/to/universe.bed")
model.train(region_sets, epochs=10, batch_size=32)

# Get embedding for a specific region
embedding = model.encode("chr1", 1000000, 1500000)
print(f"Embedding shape: {embedding.shape}")