Analyze genomic interval data for overlaps, coverage, and sequence retrieval.
npx skills add https://github.com/dralkh/seerai --skill gtars-dralkh
Please help me install this Agent Skill. Skill: gtars Source: https://github.com/dralkh/seerai/tree/main/skills/gtars Command: npx skills add https://github.com/dralkh/seerai --skill gtars-dralkh
Gtars removes the complexity of working with genomic interval data by providing fast, reliable tools for overlap analysis, coverage generation, sequence retrieval, and region set operations in research workflows.
Use the gtars skill to analyze your genomic intervals, identify overlaps, generate coverage summaries, or prepare reference-aware outputs from the files you provide.
High-intent search queries and answers about installing and using this skill.
Genomic overlap analysis detects shared regions between BED files using efficient IGD-backed queries to compare regulatory features and annotate variants. It supports parallel processing for fast interval intersection across multiple genomic interval datasets.
Coverage track generation creates WIG, BigWig, and BedGraph outputs from sequencing fragments. It processes ATAC-seq and ChIP-seq fragment files using streaming and memory-mapped file handling to produce reproducible coverage summaries.
Yes, genomic tokenization converts BED regions into tokens for genomic deep learning pipelines. This feature prepares reference-aware token outputs from genomic interval data for seamless downstream integration with geniml machine learning workflows.
Yes, both Python API and CLI access are supported for genomic interval analysis. You can integrate overlap detection, coverage generation, and refget sequence retrieval directly into reproducible genomics and machine learning pipelines.
Refget-compliant sequence retrieval computes refget digests and retrieves subsequences to validate reference genomes for reproducible bioinformatics analysis. It ensures consistent reference sequence verification across different genomic interval processing environments.
Use streaming or memory-mapped file handling during overlap analysis and coverage generation. The tool employs efficient RegionSet operations and parallel processing to manage large BED and FASTA datasets without exhausting system memory.
jimmc414Process genomic interval data with overlap detection and coverage track generation.
galeepGenerate unsupervised embeddings for genomic regions from interval data.
sanand0Analyze genomic intervals with overlap detection, coverage tracks, and ML tokenization.
pur3v4d3rAnalyze genomic intervals in BED-like datasets with Rust-backed Python bindings.
