Geniml Utilities and Additional Tools

BBClient: BED File Caching

Overview

BBClient provides efficient caching of BED files from remote sources, enabling faster repeated access and integration with R workflows.

When to Use

Use BBClient when:

• Repeatedly accessing BED files from remote databases
• Working with BEDbase repositories
• Integrating genomic data with R pipelines
• Need local caching for performance

Python Usage

from geniml.bbclient import BBClient

# Initialize client
client = BBClient(cache_folder='~/.bedcache')

# Fetch and cache BED file
bed_file = client.load_bed(bed_id='GSM123456')

# Access cached file
regions = client.get_regions('GSM123456')

R Integration

library(reticulate)
geniml <- import("geniml.bbclient")

# Initialize client
client <- geniml$BBClient(cache_folder='~/.bedcache')

# Load BED file
bed_file <- client$load_bed(bed_id='GSM123456')

Best Practices

• Configure cache directory with sufficient storage
• Use consistent cache locations across analyses
• Clear cache periodically to remove unused files

BEDshift: BED File Randomization

Overview

BEDshift provides tools for randomizing BED files while preserving genomic context, essential for generating null distributions and statistical testing.

When to Use

Use BEDshift when:

• Creating null models for statistical testing
• Generating control datasets
• Assessing significance of genomic overlaps
• Benchmarking analysis methods

Usage

from geniml.bedshift import bedshift

# Randomize BED file preserving chromosome distribution
randomized = bedshift(
    input_bed='peaks.bed',
    genome='hg38',
    preserve_chrom=True,
    n_iterations=100
)

CLI Usage

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

Randomization Strategies

Preserve chromosome distribution:

bedshift(input_bed, genome, preserve_chrom=True)

Maintains regions on same chromosomes as original.

Preserve distance distribution:

bedshift(input_bed, genome, preserve_distance=True)

Maintains inter-region distances.

Preserve region sizes:

bedshift(input_bed, genome, preserve_size=True)

Keeps original region lengths.

Best Practices

• Choose randomization strategy matching null hypothesis
• Generate multiple iterations for robust statistics
• Validate randomized output maintains desired properties
• Document randomization parameters for reproducibility

Evaluation: Model Assessment Tools

Overview

Geniml provides evaluation utilities for assessing embedding quality and model performance.

When to Use

Use evaluation tools when:

• Validating trained embeddings
• Comparing different models
• Assessing clustering quality
• Publishing model results

Embedding Evaluation

from geniml.evaluation import evaluate_embeddings

# Evaluate Region2Vec embeddings
metrics = evaluate_embeddings(
    embeddings_file='region2vec_model/embeddings.npy',
    labels_file='metadata.csv',
    metrics=['silhouette', 'davies_bouldin', 'calinski_harabasz']
)

print(f"Silhouette score: {metrics['silhouette']:.3f}")
print(f"Davies-Bouldin index: {metrics['davies_bouldin']:.3f}")

Clustering Metrics

Silhouette score: Measures cluster cohesion and separation (-1 to 1, higher better)

Davies-Bouldin index: Average similarity between clusters (≥0, lower better)

Calinski-Harabasz score: Ratio of between/within cluster dispersion (higher better)

scEmbed Cell-Type Annotation Evaluation

from geniml.evaluation import evaluate_annotation

# Evaluate cell-type predictions
results = evaluate_annotation(
    predicted=adata.obs['predicted_celltype'],
    true=adata.obs['true_celltype'],
    metrics=['accuracy', 'f1', 'confusion_matrix']
)

print(f"Accuracy: {results['accuracy']:.1%}")
print(f"F1 score: {results['f1']:.3f}")

Best Practices

• Use multiple complementary metrics
• Compare against baseline models
• Report metrics on held-out test data
• Visualize embeddings (UMAP/t-SNE) alongside metrics

Tokenization: Region Tokenization Utilities

Overview

Tokenization converts genomic regions into discrete tokens using a reference universe, enabling word2vec-style training.

When to Use

Tokenization is a required preprocessing step for:

• Region2Vec training
• scEmbed model training
• Any embedding method requiring discrete tokens

Hard Tokenization

Strict overlap-based tokenization:

from geniml.tokenization import hard_tokenization

hard_tokenization(
    src_folder='bed_files/',
    dst_folder='tokenized/',
    universe_file='universe.bed',
    p_value_threshold=1e-9
)

Parameters:

• p_value_threshold: Significance level for overlap (typically 1e-9 or 1e-6)

Soft Tokenization

Probabilistic tokenization allowing partial matches:

from geniml.tokenization import soft_tokenization

soft_tokenization(
    src_folder='bed_files/',
    dst_folder='tokenized/',
    universe_file='universe.bed',
    overlap_threshold=0.5
)

Parameters:

• overlap_threshold: Minimum overlap fraction (0-1)

Universe-Based Tokenization

Map regions to universe tokens with custom parameters:

from geniml.tokenization import universe_tokenization

universe_tokenization(
    bed_file='peaks.bed',
    universe_file='universe.bed',
    output_file='tokens.txt',
    method='hard',
    threshold=1e-9
)

Best Practices

• Universe quality: Use comprehensive, well-constructed universes
• Threshold selection: More stringent (lower p-value) for higher confidence
• Validation: Check tokenization coverage (what % of regions tokenized)
• Consistency: Use same universe and parameters across related analyses

Tokenization Coverage

Check how well regions tokenize:

from geniml.tokenization import check_coverage

coverage = check_coverage(
    bed_file='peaks.bed',
    universe_file='universe.bed',
    threshold=1e-9
)

print(f"Tokenization coverage: {coverage:.1%}")

Aim for >80% coverage for reliable training.

Text2BedNN: Search Backend

Overview

Text2BedNN creates neural network-based search backends for querying genomic regions using natural language or metadata.

When to Use

Use Text2BedNN when:

• Building search interfaces for genomic databases
• Enabling natural language queries over BED files
• Creating metadata-aware search systems
• Deploying interactive genomic search applications

Workflow

Step 1: Prepare embeddings

Train BEDspace or Region2Vec model with metadata.

Step 2: Build search index

from geniml.search import build_search_index

build_search_index(
    embeddings_file='bedspace_model/embeddings.npy',
    metadata_file='metadata.csv',
    output_dir='search_backend/'
)

Step 3: Query the index

from geniml.search import SearchBackend

backend = SearchBackend.load('search_backend/')

# Natural language query
results = backend.query(
    text="T cell regulatory regions",
    top_k=10
)

# Metadata query
results = backend.query(
    metadata={'cell_type': 'T_cell', 'tissue': 'blood'},
    top_k=10
)

Best Practices

• Train embeddings with rich metadata for better search
• Index large collections for comprehensive coverage
• Validate search relevance on known queries
• Deploy with API for interactive applications

Additional Tools

I/O Utilities

from geniml.io import read_bed, write_bed, load_universe

# Read BED file
regions = read_bed('peaks.bed')

# Write BED file
write_bed(regions, 'output.bed')

# Load universe
universe = load_universe('universe.bed')

Model Utilities

from geniml.models import save_model, load_model

# Save trained model
save_model(model, 'my_model/')

# Load model
model = load_model('my_model/')

Common Patterns

Pipeline workflow:

# 1. Build universe
universe = build_universe(coverage_folder='coverage/', method='cc', cutoff=5)

# 2. Tokenize
hard_tokenization(src_folder='beds/', dst_folder='tokens/',
                   universe_file='universe.bed', p_value_threshold=1e-9)

# 3. Train embeddings
region2vec(token_folder='tokens/', save_dir='model/', num_shufflings=1000)

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

This modular design allows flexible composition of geniml tools for diverse genomic ML workflows.