geniml

name	geniml
description	Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.
license	BSD-2-Clause

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}")
# Embedding shape: (100,)

Core API

Module 1: RegionSet — Genomic Interval I/O

Load BED files into geniml's primary data structure for downstream operations.

from geniml.io import RegionSet

# Load a BED file
rs = RegionSet("peaks.bed")
print(f"Loaded {len(rs)} regions")
print(f"First region: {rs[0]}")          # Region object: chr, start, end
print(f"Chromosomes: {set(r.chr for r in rs)}")

# Convert to list of Region objects
regions = list(rs)
for r in regions[:3]:
    print(f"  {r.chr}:{r.start}-{r.end}")

from geniml.io import RegionSet

# Create RegionSet from a list of (chr, start, end) tuples
regions_data = [
    ("chr1", 100000, 101000),
    ("chr1", 200000, 201500),
    ("chr2",  50000,  51200),
]
rs = RegionSet(regions_data)
print(f"RegionSet with {len(rs)} regions from list")

# Access by index
r = rs[0]
print(f"chr={r.chr}, start={r.start}, end={r.end}, width={r.end - r.start}")

Module 2: Universe Building

A universe defines the set of consensus regions used as the vocabulary for region2vec. Build it from a collection of BED files.

from geniml.universe import UniverseBuilder
from geniml.io import RegionSet

# Collect BED files representing diverse samples
bed_files = ["sample1.bed", "sample2.bed", "sample3.bed", "sample4.bed"]
region_sets = [RegionSet(f) for f in bed_files]

# Build universe (consensus non-overlapping regions)
builder = UniverseBuilder()
universe = builder.build(region_sets)

# Save universe to BED file
universe.to_bed("universe.bed")
print(f"Universe size: {len(universe)} consensus regions")

from geniml.universe import UniverseBuilder
from geniml.io import RegionSet

# Build universe with custom parameters
builder = UniverseBuilder(
    fraction=0.5,    # Region must appear in >= 50% of samples to be included
    merge_dist=0,    # Merge adjacent regions within this distance (bp)
)
bed_files = [f"sample_{i}.bed" for i in range(1, 11)]
region_sets = [RegionSet(f) for f in bed_files]
universe = builder.build(region_sets)
print(f"Filtered universe: {len(universe)} regions (fraction >= 0.5)")

Module 3: Region2Vec — Training Embeddings

Train word2vec-style embeddings on genomic regions, treating each BED file as a "document" and each region as a "word."

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

# Initialize model with a pre-built universe
model = Region2VecExModel(universe="universe.bed", embedding_dim=100)

# Load training data (collection of BED files = corpus)
bed_files = [f"atac_{i}.bed" for i in range(1, 51)]
region_sets = [RegionSet(f) for f in bed_files]

# Train
model.train(
    region_sets,
    epochs=20,
    batch_size=64,
    window_size=5,
    min_count=1,
)
print("Training complete")

# Save trained model
model.save("region2vec_model/")
print("Model saved to region2vec_model/")

from geniml.region2vec import Region2VecExModel

# Load a pre-trained model
model = Region2VecExModel.load("region2vec_model/")

# Encode a single genomic region
embedding = model.encode("chr1", 1_000_000, 1_500_000)
print(f"Single region embedding shape: {embedding.shape}")
# Single region embedding shape: (100,)

# Encode an entire BED file → matrix of region embeddings
from geniml.io import RegionSet
rs = RegionSet("query_peaks.bed")
embeddings = model.encode_region_set(rs)
print(f"BED file embeddings shape: {embeddings.shape}")
# BED file embeddings shape: (N_regions, 100)

Module 4: BEDSpace — Embedding Nearest-Neighbor Search

Index a corpus of BED file embeddings for fast similarity search.

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

# Build a BEDSpace index from a set of BED files
model = Region2VecExModel.load("region2vec_model/")
bed_files = [f"dataset_{i}.bed" for i in range(1, 101)]
region_sets = [RegionSet(f) for f in bed_files]

bedspace = BEDSpace(model)
bedspace.fit(region_sets, labels=[f"dataset_{i}" for i in range(1, 101)])
bedspace.save("bedspace_index/")
print(f"BEDSpace index built with {len(region_sets)} datasets")

from geniml.bedspace import BEDSpace
from geniml.io import RegionSet

# Load index and query
bedspace = BEDSpace.load("bedspace_index/")
query = RegionSet("query_sample.bed")

# Find top-k most similar datasets
results = bedspace.query(query, k=5)
for rank, (label, score) in enumerate(results, 1):
    print(f"  Rank {rank}: {label}  similarity={score:.4f}")

Module 5: Genomic Interval Datasets for ML

PyTorch-compatible Dataset classes for training ML models on genomic intervals.

from geniml.datasets import TokenizedBEDDataset
from torch.utils.data import DataLoader

# Create a tokenized dataset from multiple BED files + labels
bed_files = ["condition_A_rep1.bed", "condition_A_rep2.bed",
             "condition_B_rep1.bed", "condition_B_rep2.bed"]
labels   = [0, 0, 1, 1]

dataset = TokenizedBEDDataset(
    bed_files=bed_files,
    universe="universe.bed",
    labels=labels,
)
print(f"Dataset size: {len(dataset)} samples")

# Use with PyTorch DataLoader
loader = DataLoader(dataset, batch_size=8, shuffle=True)
for batch_tokens, batch_labels in loader:
    print(f"Batch tokens shape: {batch_tokens.shape}")
    print(f"Batch labels: {batch_labels}")
    break

from geniml.datasets import RegionEmbeddingDataset
from geniml.region2vec import Region2VecExModel
import torch

# Pre-embed BED files and create a dataset for a classifier
model = Region2VecExModel.load("region2vec_model/")
bed_files = ["pos_1.bed", "pos_2.bed", "neg_1.bed", "neg_2.bed"]
labels    = [1, 1, 0, 0]

emb_dataset = RegionEmbeddingDataset(
    bed_files=bed_files,
    model=model,
    labels=labels,
    aggregation="mean",    # aggregate region embeddings per sample
)
X, y = emb_dataset[0]
print(f"Sample embedding shape: {X.shape}, label: {y}")
# Sample embedding shape: (100,), label: 1

Module 6: Embedding Evaluation

Assess embedding quality using neighborhood overlap and intrinsic metrics.

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

model = Region2VecExModel.load("region2vec_model/")
bed_files = [f"sample_{i}.bed" for i in range(1, 21)]
region_sets = [RegionSet(f) for f in bed_files]

evaluator = EmbeddingEvaluator(model)
metrics = evaluator.evaluate(region_sets)

print(f"Neighborhood overlap score: {metrics['neighborhood_overlap']:.4f}")
print(f"Silhouette score:           {metrics.get('silhouette', 'N/A')}")
# Higher neighborhood overlap → more biologically coherent embeddings

Key Concepts

region2vec Training Analogy

Region2vec treats each BED file as a "sentence" (a document of co-occurring genomic regions) and each genomic region token (from the universe) as a "word." Word2vec's skip-gram objective is applied: regions that frequently co-occur in BED files are learned to have similar embeddings. This means two regions that tend to be open/active in the same set of samples will have nearby embeddings, enabling meaningful similarity search without explicit labels.

# Conceptual analogy: universe regions = vocabulary tokens
# BED file = sentence = [region_token_1, region_token_2, ...]
# Training: predict co-occurring regions within a sliding window
from geniml.io import RegionSet
rs = RegionSet("sample.bed")
print(f"This BED file contains {len(rs)} 'words' (tokens) from the universe vocabulary")

Universe as Vocabulary

The universe is a non-overlapping, genome-wide set of consensus regions that serves as the token vocabulary for region2vec. A well-chosen universe should cover the genomic regions present in your dataset while being compact enough for efficient training. Regions in a BED file that do not overlap the universe are ignored during training.

Common Workflows

Workflow 1: Train and Search Embeddings from ATAC-seq Peaks

Goal: Embed ATAC-seq peak sets, then find samples most similar to a query.

from geniml.universe import UniverseBuilder
from geniml.region2vec import Region2VecExModel
from geniml.bedspace import BEDSpace
from geniml.io import RegionSet
from pathlib import Path

# Step 1: Collect BED files
bed_dir = Path("atac_peaks/")
bed_files = sorted(bed_dir.glob("*.bed"))
region_sets = [RegionSet(str(f)) for f in bed_files]
labels = [f.stem for f in bed_files]
print(f"Loaded {len(region_sets)} ATAC-seq peak sets")

# Step 2: Build universe
builder = UniverseBuilder(fraction=0.3)
universe = builder.build(region_sets)
universe.to_bed("atac_universe.bed")
print(f"Universe: {len(universe)} regions")

# Step 3: Train region2vec
model = Region2VecExModel(universe="atac_universe.bed", embedding_dim=100)
model.train(region_sets, epochs=15, batch_size=64, window_size=5)
model.save("atac_region2vec/")

# Step 4: Build BEDSpace index
bedspace = BEDSpace(model)
bedspace.fit(region_sets, labels=labels)
bedspace.save("atac_bedspace/")

# Step 5: Query with a new sample
query = RegionSet("new_sample.bed")
results = bedspace.query(query, k=5)
print("Top 5 most similar samples:")
 rank, (label, score)  (results, ):
    ()

Workflow 2: ML Classification on Genomic Intervals

Goal: Train a logistic regression classifier on region2vec embeddings to classify cell types.

from geniml.region2vec import Region2VecExModel
from geniml.datasets import RegionEmbeddingDataset
from geniml.io import RegionSet
from torch.utils.data import DataLoader
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import numpy as np

model = Region2VecExModel.load("atac_region2vec/")

# Prepare labeled dataset (e.g., T-cell vs B-cell ATAC peaks)
bed_files = ["tcell_1.bed", "tcell_2.bed", "tcell_3.bed",
             "bcell_1.bed", "bcell_2.bed", "bcell_3.bed"]
labels    = [0, 0, 0, 1, 1, 1]

dataset = RegionEmbeddingDataset(
    bed_files=bed_files, model=model, labels=labels, aggregation="mean"
)

# Collect embeddings
X = np.array([dataset[i][0].numpy() for i in range(len(dataset))])
y = np.array([dataset[i][1]         for i in range(len(dataset))])
print(f"Feature matrix: {X.shape}, labels: {y}")

# Cross-validate a simple classifier
clf = LogisticRegression(max_iter=1000)
scores = cross_val_score(clf, X, y, cv=, scoring=)
()

Key Parameters

Parameter	Module	Default	Range / Options	Effect
embedding_dim	Region2Vec	100	50–512	Dimensionality of region embeddings; higher = more expressive but slower
window_size	Region2Vec training	5	2–20	Context window for skip-gram co-occurrence; larger = broader co-occurrence
epochs	Region2Vec training	10	5–100	Training iterations; increase for large datasets
batch_size	Region2Vec training	32	16–512	Mini-batch size; larger batches improve stability
fraction	UniverseBuilder	0.5	0.0–1.0	Minimum fraction of samples a region must appear in to enter the universe
aggregation	RegionEmbeddingDataset	"mean"	"mean", "sum", "max"	How per-region embeddings are pooled into a sample-level vector
k	BEDSpace query	10	1–corpus size	Number of nearest neighbors to return

Common Recipes

Recipe: Load a Pre-Trained Hugging Face region2vec Model

When to use: Apply a community-trained model without local training.

from geniml.region2vec import Region2VecExModel

# Load a pre-trained model from HuggingFace Hub
model = Region2VecExModel("databio/r2v-ChIP-atlas-hg38-v2")
print(f"Model embedding dim: {model.embedding_dim}")

from geniml.io import RegionSet
rs = RegionSet("my_peaks.bed")
embeddings = model.encode_region_set(rs)
print(f"Embeddings shape: {embeddings.shape}")

Recipe: UMAP Visualization of Sample Embeddings

When to use: Visually inspect clustering of samples in embedding space.

from geniml.region2vec import Region2VecExModel
from geniml.io import RegionSet
import numpy as np

model = Region2VecExModel.load("atac_region2vec/")

bed_files = [f"sample_{i}.bed" for i in range(1, 21)]
sample_embeddings = []
for f in bed_files:
    rs = RegionSet(f)
    emb = model.encode_region_set(rs)
    sample_embeddings.append(emb.mean(axis=0))   # mean-pool regions

X = np.array(sample_embeddings)
print(f"Sample embedding matrix: {X.shape}")

try:
    import umap
    import matplotlib.pyplot as plt
    reducer = umap.UMAP(n_components=2, random_state=42)
    X_2d = reducer.fit_transform(X)
    plt.figure(figsize=(6, 5))
    plt.scatter(X_2d[:, 0], X_2d[:, 1], s=40)
    for i, f in enumerate(bed_files):
        plt.annotate(f, (X_2d[i, 0], X_2d[i, 1]), fontsize=7)
    plt.title("Sample embeddings (UMAP)")
    plt.savefig("sample_umap.png", dpi=150, bbox_inches="tight")
    print("Saved sample_umap.png")
except ImportError:
    print("Install umap-learn for UMAP visualization")

Troubleshooting

Problem	Cause	Solution
FileNotFoundError on universe	Universe BED path incorrect or not built yet	Run UniverseBuilder.build() and save with .to_bed() before training
All embeddings identical or near-zero	Universe has no overlap with training BED files	Verify BED coordinate system matches universe (hg38 vs hg19, chr prefix)
RuntimeError: CUDA out of memory	Batch size too large for GPU	Reduce batch_size or use CPU training
Very low neighborhood overlap score	Too few training samples or too many epochs (overfitting)	Use ≥20 BED files; tune epochs; try smaller embedding_dim
BEDSpace query returns no results	Query regions not present in index universe	Check query BED overlaps universe; use fraction parameter to broaden universe
Slow training	Large universe + many BED files	Reduce universe size with higher fraction threshold; use GPU

Related Skills

• gtars — fast BED file I/O and interval operations for preprocessing before geniml training
• scanpy-scrna-seq — downstream clustering and UMAP visualization of embeddings stored in AnnData

References

• Geniml documentation — official docs and tutorials
• Geniml GitHub repository — source code and examples
• PyPI: geniml — installation and version info
• HuggingFace: databio region2vec models — pre-trained region2vec model collection