gtars

Detect overlaps between genomic intervals in BED data.

16|7|Updated Nov 20, 2025
One-click install
npx skills add https://github.com/jackspace/ClaudeSkillz --skill gtars
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gtars
Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-gtars
Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill gtars

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a high-performance Rust toolkit for genomic interval analysis with Python bindings, including overlap detection, coverage generation, tokenization for ML models, and reference sequence management.

Core Features & Use Cases

  • Overlap detection and IGD indexing
  • Coverage track generation with uniwig
  • Genomic tokenization for ML workflows
  • Reference sequence management via refget
  • Fragment processing and scoring

Quick Start

  1. Install Rust tools and Python bindings
  2. Use IGD indexing or tokenization in your genomic pipeline
  3. Validate outputs with the provided references

Frequently Asked Questions about gtars

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect overlaps between genomic intervals in BED files?

Overlap detection in BED files identifies where genomic intervals intersect. gtars uses a high-performance Rust core to efficiently find overlapping features, returning overlap sets that reveal which intervals intersect across your dataset.

Can I use genomic tokenization for machine learning models?

Yes. Genomic tokenization converts interval data into tokens suitable for ML workflows. gtars tokenizes BED intervals and reference sequences, enabling you to feed standardized genomic tokens directly into machine learning pipelines.

What's the best way to generate coverage tracks from genomic data?

Coverage track generation computes read depth across intervals. gtars generates coverage tracks via uniwig output from BED input, converting interval data into continuous coverage representations for visualization and downstream analysis.

Does gtars work with standard genomic file formats like FASTA and BigWig?

Yes. gtars supports standard genomic formats including BED, WIG, BigWig, FASTA, and fragment TSV. This format compatibility lets you integrate gtars into existing pipelines without converting your reference sequences or interval data.

How do I index genomic intervals for fast queries?

IGD indexing creates queryable interval indexes. gtars builds IGD indexes from BED data, enabling rapid interval lookups and overlap detection—essential for large-scale genomic analyses and single-cell fragment processing.

Can I access gtars functionality from Python or the command line?

Both. gtars provides Python bindings and a CLI interface to its Rust core, letting you call overlap detection, tokenization, and coverage generation either programmatically or as command-line tools.