Perform end-to-end genomic interval analysis and tokenization with a Rust core and Python bindings.
npx skills add https://github.com/mkurman/tamux --skill gtars-mkurman
Please help me install this Agent Skill. Skill: gtars Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/gtars Command: npx skills add https://github.com/mkurman/tamux --skill gtars-mkurman
gtars provides fast, end-to-end genomic interval analysis and tokenization tools for scalable bioinformatics workflows, unifying overlap, coverage, and reference sequence tasks under a single framework.
Install the gtars Python package and run a basic RegionSet example to compute overlaps and generate a coverage track.
High-intent search queries and answers about installing and using this skill.
Detect overlapping genomic intervals using IGD indexing, which enables rapid querying of overlapping regions across large genomic datasets. The Rust core with Python bindings processes BED data to return matched interval sets for downstream bioinformatics workflows.
Genomic tokenization for ML workflows is the process of converting genomic intervals and sequences into input formats suitable for transformer models. It prepares high-throughput genomic data for machine learning pipelines by partitioning intervals into standardized tokens.
Generate coverage tracks via the uniwig module, which supports conversion to WIG and BigWig formats for genomic visualization. It processes input interval data to produce tracks representing per-base or region coverage across reference sequences.
Yes, you can manage reference sequences using the Python API with FASTA data inputs. The framework provides GA4GH refget compatibility for on-demand sequence retrieval, allowing Python pipelines to query and retrieve genomic sequences dynamically.
Yes, the Rust core architecture ensures fast, scalable genomic interval analysis tailored for high-throughput workflows. It handles large-scale genomic datasets efficiently by applying IGD indexing and modular processing across BED, WIG, BigWig, and FASTA formats.
The primary limitation of using a Rust core with Python bindings is the compilation and environment setup overhead required to bridge the two languages. Users must ensure proper Rust toolchain configuration to utilize the fast interval processing capabilities.
jimmc414Process genomic interval data with overlap detection and coverage track generation.
ovachieverProcess genomic BED intervals with a Rust toolkit and Python bindings.
jasrajtulsiAnalyze genomic interval overlaps and generate coverage tracks from BED files.
hxk622Manipulate genomic interval data with overlap detection and coverage track generation.
