gtars

Genomic interval overlap detection on BED files is handled by building an IGD index, which enables efficient, multi-threaded queries to find overlapping regions across thousands of experimental peaks.

Yes, coverage track generation from single-cell ATAC-seq fragment files is supported. The toolkit processes fragments to output BigWig coverage tracks using Rust-backed performance for high-throughput data.

Genomic tokenization for machine learning preprocessing converts genomic regions into tokens suitable for deep learning models. This is paired with sequence management via the GA4GH refget protocol for reference retrieval.

Interval analysis and sequence management require integration with standard genomic formats including BED for interval manipulation, WIG for coverage tracks, and FASTA for reference sequence handling.

Yes, the toolkit supports multi-threaded data operations designed to address computational bottlenecks in large-scale genomic data processing, enabling fast overlap detection and track generation across thousands of peaks.

Reference sequence management uses the GA4GH refget protocol to retrieve and manage sequences. This integrates with FASTA files to maintain consistent reference genomes during ML preprocessing and interval operations.