What problem does it solve?
polars-bio addresses the need for fast, scalable, and efficient genomic interval processing, significantly reducing computational time and resource usage compared to traditional Python-based tools.
Core Features & Use Cases
- Genomic Range Arithmetic: Enables operations like overlap, nearest, merge, and coverage analyses to interpret genomic intervals accurately and rapidly.
- Bioinformatics File I/O: Supports reading and writing common formats such as BED, VCF, BAM, CRAM, GFF, GTF, FASTA, FASTQ, and SAM, facilitating large-scale genomic data workflows.
- SQL Data Integration: Allows querying bioinformatics files with specialized SQL statements for flexible and powerful data exploration across large datasets.
- Streaming & Out-of-Core: Handles datasets larger than memory via lazy evaluation and DataFusion's streaming capabilities, making analyses scalable to whole genomes.
- Use Case Example: Efficiently computing read coverage across a 30x human whole genome dataset or performing large-scale interval overlaps for variant annotation.
Quick Start
Use polars-bio to load a VCF file, perform an overlap analysis with a BED file, and retrieve the results as a DataFrame without heavy memory usage.
