polars-bio

Perform genomic interval arithmetic and bioinformatics file I/O on Polars DataFrames.

1|Updated Mar 4, 2026
One-click install
npx skills add https://github.com/Hung-3008/agusta --skill polars-bio-hung-3008
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: polars-bio
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/polars-bio
Command: npx skills add https://github.com/Hung-3008/agusta --skill polars-bio-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

High-performance genomic interval operations and bioinformatics I/O on Polars DataFrames, enabling fast overlap, nearest, merge, coverage, complement, and subtract workflows for BED, VCF, BAM, GFF among others with streaming and cloud-native capabilities.

Core Features & Use Cases

  • Genomic interval operations (overlap, nearest, merge, coverage, complement, subtract) on Polars DataFrames with optional Lazy/streaming execution and DataFusion acceleration.
  • Bioinformatics file I/O support for BED/VCF/BAM/CRAM/GFF/GTF/FASTA/FASTQ/SAM and cloud storage with compression.
  • SQL integration via DataFusion to query registered files as tables and combine with interval operations.
  • Pileup depth computation from BAM/CRAM files and large-scale genomic data processing.
  • Use case: process large genomics datasets that exceed memory, migrate from slower bioframe alternatives, and enable cloud-native pipelines.

Quick Start

Install polars-bio and run a quick overlap example on two interval sets to validate results.

Frequently Asked Questions about polars-bio

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

FAQPage Schema
How do I perform genomic interval operations like overlap and merge on Polars DataFrames?

Genomic interval operations on Polars DataFrames are performed using modular functional and method-chaining APIs to execute overlap, nearest, merge, coverage, complement, and subtract workflows with high-speed DataFusion acceleration.

Can I process VCF, BAM, and BED files directly from cloud storage?

Yes, you can stream VCF, BAM, and BED files directly from cloud-native S3, GCS, or Azure storage with compression support, enabling high-speed bioinformatics I/O without downloading entire datasets locally first.

What is the best way to handle genomic datasets that exceed available memory?

Streaming I/O with optional Lazy execution allows you to process large-scale genomic datasets that exceed memory limits by performing interval operations and file I/O incrementally rather than loading everything into memory.

Does this support SQL queries for bioinformatics file formats?

SQL integration via DataFusion allows you to query registered bioinformatics files as tables and combine SQL logic directly with genomic interval operations on Polars DataFrames for complex analytical workflows.

How does coordinate-system metadata management improve genomic interval arithmetic?

Coordinate-system metadata management ensures accurate genomic interval arithmetic results by tracking and aligning coordinate frameworks across overlapping, nearest, and coverage operations performed on DataFrames.

Can I compute pileup depth from BAM and CRAM files using Polars?

Pileup depth computation from BAM and CRAM files is supported natively, allowing you to calculate read depth directly within large-scale genomic data processing pipelines on Polars DataFrames.