Agent skill · research science · aiskillstore
gtars
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Why this skill is useful
Provides specialized commands for genomic interval analysis and machine learning preprocessing that the AI wouldn't reliably generate on its own.
What it needs
Requires cargo, rust, uv installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Gtars: Genomic Tools and Algorithms in Rust Overview Gtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management. Use this skill when working with: Genomic interval files (BED format) Overlap detection between genomic regions Coverage track generation (WIG, BigWig) Genomic ML preprocessing and tokenization Fragment analysis in single-cell genomics Reference sequence retrieval and validation Installation Python Installation Install gtars Python bindings: CLI Installation Install command-line tools (requires Rust/Cargo): Rust Library Add to Cargo.toml for Rust projects: Core Capabilities Gtars is organized into specialized modules, each focused on specific genomic analysis tasks: 1. Overlap Detection and IGD Indexing Efficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure. When to use: Finding overlapping regulatory elements Variant annotation Comparing ChIP-seq peaks Identifying shared genomic features Quick example: See references/overlap.md for comprehensive overlap detection documentation. 2. Coverage Track Generation Generate coverage tracks from sequencing data with the uniwig module. When to use: ATAC-seq accessibility profiles ChIP-seq coverage visualization RNA-seq read coverage Differential coverage analysis Quick example: See references/coverage.md for detailed coverage analysis workflows. 3. Genomic Tokenization Convert genomic regions into discrete tokens for machine learning applications, particularly for deep learning models on genomic data. When to use: Preprocessing for genomic ML models Integration with geniml library Creating position encodings Training transformer models on genomic sequences Quick example: See references/tokenizers.md for tokenization documentation. 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/gtars