Utilities

# 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 ```python 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 ```r library(reticulate) geniml <- import("geniml.bbclient") # Initialize client client <- geniml$BBClient(cache_folder='~/.bedcache') # Load BED file bed_file 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** ```python 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** ```python 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 ```python 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 ```python 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:** ```python # 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.