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.
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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.
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Stars31,545
Forks3,146
LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
</> SKILL.md
gtars.SKILL.md
---name: gtars
description: 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.
license: Unknown
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---# 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:
```bash
uv pip install gtars
```
### CLI Installation
Gtars serves as the foundation for the geniml Python package, providing core genomic interval operations for machine learning workflows. When working on geniml-related tasks, use gtars for data preprocessing and tokenization.
## Performance Characteristics
- **Native Rust performance**: Fast execution with low memory overhead
- **Parallel processing**: Multi-threaded operations for large datasets
- **Memory efficiency**: Streaming and memory-mapped file support
- **Zero-copy operations**: NumPy integration with minimal data copying
## Data Formats
Gtars works with standard genomic formats:
- **BED**: Genomic intervals (3-column or extended)
- **WIG/BigWig**: Coverage tracks
- **FASTA**: Reference sequences
- **Fragment TSV**: Single-cell fragment files with barcodes
