Gtars

Streamline and integrate Gtars automation into your existing systems

Category: productivity Source: K-Dense-AI/claude-scientific-skills

GTARS is a community skill for analyzing genomic region sets using the GTARS toolkit, covering interval operations, region overlap analysis, tokenization, universe management, and integration with BED file workflows for computational genomics.

What Is This?

Overview

GTARS provides patterns for performing operations on genomic interval data represented as BED files. It covers region set loading and validation from standard BED format files, interval operations including merge, intersect, subtract, and complement, region set tokenization that converts intervals into discrete tokens for ML models, universe construction that defines the complete set of possible genomic regions, and overlap statistics that quantify similarity between region sets. The skill enables bioinformaticians to build efficient genomic interval analysis pipelines with a Rust-backed Python interface for performance.

Who Should Use This

This skill serves bioinformaticians processing large collections of genomic region sets, ML researchers tokenizing genomic intervals for language model applications, and computational biologists performing overlap analysis across epigenomic experiments such as ChIP-seq, ATAC-seq, or Hi-C datasets.

Why Use It?

Problems It Solves

Genomic interval operations on large BED files are slow when implemented in pure Python. Tokenizing variable-length region sets into fixed vocabularies for ML models requires consistent universe definitions. Computing overlap statistics between thousands of region set pairs needs efficient algorithms. Managing genomic universes for reproducible tokenization requires standardized tools that enforce coordinate consistency across experiments and collaborators.

Core Highlights

Rust backend provides high-performance interval operations for large genomic datasets. Tokenizer converts BED regions into integer tokens using configurable universe files. Overlap calculator computes Jaccard and other similarity metrics between region sets. Universe builder creates consensus region sets from collections of BED files, enabling reproducible vocabulary construction across independent analyses.

How to Use It?

Basic Usage

from gtars import RegionSet, Universe
from gtars.tokenizers import TreeTokenizer

rs = RegionSet("peaks.bed")
print(f"Regions: {len(rs)}")

universe = Universe("universe.bed")
print(f"Universe size: {len(universe)}")

tokenizer = TreeTokenizer(universe)
tokens = tokenizer.tokenize(rs)
print(f"Tokens: {len(tokens)}")
print(f"First 10: {tokens[:10]}")

rs2 = RegionSet("peaks_condition.bed")
overlap = rs.overlap(rs2)
print(f"Overlap count: {overlap}")
print(f"Jaccard: {rs.jaccard(rs2):.4f}")

Real-World Examples

from gtars import RegionSet, Universe
from gtars.tokenizers import TreeTokenizer
import os

class RegionAnalysisPipeline:
    def __init__(self, universe_path: str):
        self.universe = Universe(universe_path)
        self.tokenizer = TreeTokenizer(
            self.universe)

    def batch_tokenize(
            self, bed_dir: str) -> dict:
        results = {}
        for f in os.listdir(bed_dir):
            if not f.endswith(".bed"):
                continue
            path = os.path.join(bed_dir, f)
            rs = RegionSet(path)
            tokens = self.tokenizer.tokenize(rs)
            results[f] = {
                "n_regions": len(rs),
                "n_tokens": len(tokens),
                "tokens": tokens}
        return results

    def pairwise_jaccard(
            self, bed_files: list[str]
            ) -> list[dict]:
        sets = [(f, RegionSet(f))
                for f in bed_files]
        pairs = []
        for i in range(len(sets)):
            for j in range(i + 1, len(sets)):
                jac = sets[i][1].jaccard(
                    sets[j][1])
                pairs.append({
                    "file_a": sets[i][0],
                    "file_b": sets[j][0],
                    "jaccard": round(jac, 4)})
        return sorted(pairs,
            key=lambda x: x["jaccard"],
            reverse=True)

pipeline = RegionAnalysisPipeline("universe.bed")
tokens = pipeline.batch_tokenize("bed_files/")
for name, data in tokens.items():
    print(f"{name}: {data['n_tokens']} tokens")

Advanced Tips

Use TreeTokenizer for faster lookups when tokenizing against large universes with millions of regions. Build consensus universes from your specific dataset collection for analysis-appropriate tokenization rather than relying on generic reference universes. Cache tokenized representations when running multiple analyses on the same region sets to avoid redundant computation and reduce pipeline runtime.

When to Use It?

Use Cases

Build a region set tokenization pipeline for training genomic language models on epigenomic data. Create a pairwise similarity matrix for clustering ChIP-seq experiments by binding overlap. Implement a quality control tool that validates BED files against a reference universe.

Important Notes

Requirements

Python with the gtars package installed. A universe BED file defining the region vocabulary for tokenization. BED files containing genomic regions as input data.

Usage Recommendations

Do: use a consistent universe file across all region sets in a single analysis for comparable tokenization. Validate BED file format and coordinate sorting before processing. Leverage the Rust backend for batch operations on large collections.

Don't: mix region sets from different genome assemblies without coordinate liftover. Use tokenization without a properly constructed universe that covers your data. Assume Jaccard similarity captures all aspects of biological similarity between region sets.

Limitations

Universe quality directly affects tokenization resolution and downstream analysis results. Very large pairwise comparisons may still require significant time even with the Rust backend. Some advanced interval operations may need complementary tools like bedtools for full functionality.