Does it support single-cell data analysis?

Yes, it includes specialized tools for splitting fragment files by cell barcodes and scoring fragment overlaps against reference datasets.

How does Gtars handle large genomic datasets?

Gtars is built in Rust, leveraging native performance, multi-threading, and memory-efficient streaming to process massive genomic files with low overhead.

What is Gtars used for?

Gtars is a high-performance toolkit for genomic interval analysis, including overlap detection, coverage generation, and machine learning preprocessing.

Can I use Gtars with Python?

Yes, Gtars provides comprehensive Python bindings and integrates seamlessly with NumPy and the geniml library for programmatic control.

What genomic file formats are supported?

Gtars supports standard formats including BED (intervals), WIG/BigWig (coverage), FASTA (sequences), and TSV fragment files.

Gtars Genomics Toolkit

Name: Gtars Genomics Toolkit
Author: Activer007

byActiver007

Ciencia de Datos y ML

Performs high-performance genomic interval analysis and data preprocessing for machine learning using Rust-powered tools.

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Gtars is a specialized Claude Code skill designed for computational biologists and bioinformaticians working with large-scale genomic data. Built on a high-performance Rust core with Python bindings, it excels at tasks such as overlap detection, coverage track generation, and genomic tokenization. It provides advanced capabilities for processing BED files, managing reference sequences via the GA4GH refget protocol, and preparing single-cell fragment data for machine learning models. Whether you are building genomic ML pipelines or analyzing regulatory elements, Gtars offers the speed and memory efficiency required for modern bioinformatics workflows.

Características Principales

Generation of WIG and BigWig coverage tracks from sequencing data
High-speed overlap detection using IGD indexing for regulatory element analysis
Genomic tokenization for training transformer and deep learning models
Single-cell fragment processing and quality control scoring
Reference sequence management with GA4GH refget protocol support
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Casos de Uso

Generating visualization-ready coverage tracks from ATAC-seq fragments
Preprocessing genomic regions and creating position encodings for ML pipelines
Identifying overlapping features between ChIP-seq peaks and promoters

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Características Principales

Generation of WIG and BigWig coverage tracks from sequencing data
High-speed overlap detection using IGD indexing for regulatory element analysis
Genomic tokenization for training transformer and deep learning models
Single-cell fragment processing and quality control scoring
Reference sequence management with GA4GH refget protocol support
0 GitHub stars

Casos de Uso

Generating visualization-ready coverage tracks from ATAC-seq fragments
Preprocessing genomic regions and creating position encodings for ML pipelines
Identifying overlapping features between ChIP-seq peaks and promoters