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:

Tokenize BED files using a universe reference

Train Region2Vec model on tokens

Generate embeddings for regions

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

2. BEDspace: Joint Region and Metadata Embeddings

Train shared embeddings for region sets and metadata labels using StarSpace.

Use for: Metadata-aware searches, cross-modal queries (region→label or label→region), joint analysis of genomic content and experimental conditions.

Workflow:

Preprocess regions and metadata

Train BEDspace model

Compute distances

Query across regions and labels

Reference: See references/bedspace.md for detailed workflow, search types, and examples.

3. scEmbed: Single-Cell Chromatin Accessibility Embeddings

Train Region2Vec models on single-cell ATAC-seq data for cell-level embeddings.

Use for: scATAC-seq clustering, cell-type annotation, dimensionality reduction of single cells, integration with scanpy workflows.

Workflow:

Prepare AnnData with peak coordinates

Pre-tokenize cells

Train scEmbed model

Generate cell embeddings

Cluster and visualize with scanpy

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

4. Consensus Peaks: Universe Building

Build reference peak sets (universes) from BED file collections using multiple statistical methods.

Use for: Creating tokenization references, standardizing regions across datasets, defining consensus features with statistical rigor.

Workflow:

Combine BED files

Generate coverage tracks

Build universe using CC, CCF, ML, or HMM method

Methods:

CC (Coverage Cutoff): Simple threshold-based

CCF (Coverage Cutoff Flexible): Confidence intervals for boundaries

ML (Maximum Likelihood): Probabilistic modeling of positions

HMM (Hidden Markov Model): Complex state modeling

Reference: See references/consensus_peaks.md for method comparison, parameters, and examples.

5. Utilities: Supporting Tools

Additional tools for caching, randomization, evaluation, and search.

Available utilities:

BBClient: BED file caching for repeated access

BEDshift: Randomization preserving genomic context

Evaluation: Metrics for embedding quality (silhouette, Davies-Bouldin, etc.)

Tokenization: Region tokenization utilities (hard, soft, universe-based)

Text2BedNN: Neural search backends for genomic queries

Reference: See references/utilities.md for detailed usage of each utility.

Common Workflows

Basic Region Embedding Pipeline

from geniml.tokenization import hard_tokenization
from geniml.region2vec import region2vec
from geniml.evaluation import evaluate_embeddings
Step 1: Tokenize BED files

hard_tokenization(
    src_folder='bed_files/',
    dst_folder='tokens/',
    universe_file='universe.bed',
    p_value_threshold=1e-9
)
Step 2: Train Region2Vec

region2vec(
    token_folder='tokens/',
    save_dir='model/',
    num_shufflings=1000,
    embedding_dim=100
)
Step 3: Evaluate

metrics = evaluate_embeddings(
    embeddings_file='model/embeddings.npy',
    labels_file='metadata.csv'
)

scATAC-seq Analysis Pipeline

import scanpy as sc
from geniml.scembed import ScEmbed
from geniml.io import tokenize_cells
Step 1: Load data

adata = sc.read_h5ad('scatac_data.h5ad')
Step 2: Tokenize cells

tokenize_cells(
    adata='scatac_data.h5ad',
    universe_file='universe.bed',
    output='tokens.parquet'
)
Step 3: Train scEmbed

model = ScEmbed(embedding_dim=100)
model.train(dataset='tokens.parquet', epochs=100)
Step 4: Generate embeddings

embeddings = model.encode(adata)
adata.obsm['scembed_X'] = embeddings
Step 5: Cluster with scanpy

sc.pp.neighbors(adata, use_rep='scembed_X')
sc.tl.leiden(adata)
sc.tl.umap(adata)

Universe Building and Evaluation

# Generate coverage
cat bed_files/*.bed > combined.bed
uniwig -m 25 combined.bed chrom.sizes coverage/
Build universe with coverage cutoff

geniml universe build cc \
  --coverage-folder coverage/ \
  --output-file universe.bed \
  --cutoff 5 \
  --merge 100 \
  --filter-size 50
Evaluate universe quality

geniml universe evaluate \
  --universe universe.bed \
  --coverage-folder coverage/ \
  --bed-folder bed_files/

CLI Reference

Geniml provides command-line interfaces for major operations:

# Region2Vec training
geniml region2vec --token-folder tokens/ --save-dir model/ --num-shuffle 1000
BEDspace preprocessing

geniml bedspace preprocess --input regions/ --metadata labels.csv --universe universe.bed
BEDspace training

geniml bedspace train --input preprocessed.txt --output model/ --dim 100
BEDspace search

geniml bedspace search -t r2l -d distances.pkl -q query.bed -n 10
Universe building

geniml universe build cc --coverage-folder coverage/ --output universe.bed --cutoff 5
BEDshift randomization

geniml bedshift --input peaks.bed --genome hg38 --preserve-chrom --iterations 100

When to Use Which Tool

Use Region2Vec when:

Working with bulk genomic data (ChIP-seq, ATAC-seq, etc.)

Need unsupervised embeddings without metadata

Comparing region sets across experiments

Building features for downstream supervised learning

Use BEDspace when:

Metadata labels available (cell types, tissues, conditions)

Need to query regions by metadata or vice versa

Want joint embedding space for regions and labels

Building searchable genomic databases

Use scEmbed when:

Analyzing single-cell ATAC-seq data

Clustering cells by chromatin accessibility

Annotating cell types from scATAC-seq

Integration with scanpy is desired

Use Universe Building when:

Need reference peak sets for tokenization

Combining multiple experiments into consensus

Want statistically rigorous region definitions

Building standard references for a project

Use Utilities when:

Need to cache remote BED files (BBClient)

Generating null models for statistics (BEDshift)

Evaluating embedding quality (Evaluation)

Building search interfaces (Text2BedNN)

Best Practices

General Guidelines

Universe quality is critical: Invest time in building comprehensive, well-constructed universes

Tokenization validation: Check coverage (>80% ideal) before training

Parameter tuning: Experiment with embedding dimensions, learning rates, and training epochs

Evaluation: Always validate embeddings with multiple metrics and visualizations

Documentation: Record parameters and random seeds for reproducibility

Performance Considerations

Pre-tokenization: For scEmbed, always pre-tokenize cells for faster training

Memory management: Large datasets may require batch processing or downsampling

Computational resources: ML/HMM universe methods are computationally intensive

Model caching: Use BBClient to avoid repeated downloads

Integration Patterns

With scanpy: scEmbed embeddings integrate seamlessly as adata.obsm entries

With BEDbase: Use BBClient for accessing remote BED repositories

With Hugging Face: Export trained models for sharing and reproducibility

With R: Use reticulate for R integration (see utilities reference)

Related Projects

Geniml is part of the BEDbase ecosystem:

BEDbase: Unified platform for genomic regions

BEDboss: Processing pipeline for BED files

Gtars: Genomic tools and utilities

BBClient: Client for BEDbase repositories

Additional Resources

Documentation: https://docs.bedbase.org/geniml/

GitHub: https://github.com/databio/geniml

Pre-trained models: Available on Hugging Face (databio organization)

Publications: Cited in documentation for methodological details

Troubleshooting

"Tokenization coverage too low":

Check universe quality and completeness

Adjust p-value threshold (try 1e-6 instead of 1e-9)

Ensure universe matches genome assembly

"Training not converging":

Adjust learning rate (try 0.01-0.05 range)

Increase training epochs

Check data quality and preprocessing

"Out of memory errors":

Reduce batch size for scEmbed

Process data in chunks

Use pre-tokenization for single-cell data

"StarSpace not found" (BEDspace):

Install StarSpace separately: https://github.com/facebookresearch/StarSpace

Set --path-to-starspace parameter correctly

For detailed troubleshooting and method-specific issues, consult the appropriate reference file.

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