High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bio…
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name: polars-bio
description: High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.
license: Apache-2.0
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.11–3.14 and polars-bio (uv pip install). Cloud I/O uses standard AWS/GCS/Azure SDK env vars when paths use s3://, gs://, or az:// URIs.
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---
# polars-bio
## Overview
polars-bio is a high-performance Python library for genomic interval operations and bioinformatics file I/O, built on Polars, Apache Arrow, and Apache DataFusion. It provides a familiar DataFrame-centric API for interval arithmetic (overlap, nearest, merge, coverage, complement, subtract) and reading/writing common bioinformatics formats (BED, VCF, BAM, CRAM, GFF/GTF, FASTA, FASTQ).
Key value propositions:
- **6-38x faster** than bioframe on real-world genomic benchmarks
- **Streaming/out-of-core** support for large genomes via DataFusion
- **Cloud-native** file I/O (S3, GCS, Azure) with predicate pushdown
- **Two API styles**: functional (`pb.overlap(df1, df2)`) and method-chaining (`df1.lazy().pb.overlap(df2)`)
- **SQL interface** for genomic data via DataFusion SQL engine
## When to Use This Skill
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