polars-bio

You can perform genomic interval overlap operations by loading two interval DataFrames and applying the overlap command to output a CSV of intersecting ranges. The Skill executes high-performance range arithmetic directly on Polars DataFrames.

Yes, you can read and write VCF, BAM, CRAM, BED, GFF/GTF, FASTA, and FASTQ files directly using Polars DataFrames. The Skill handles bioinformatics file I/O natively, supporting local and cloud storage on S3, GCS, and Azure.

The best way to merge large genomic intervals is using the built-in merge operation optimized for Polars DataFrames. This approach significantly reduces processing times for large genomic datasets compared to standard Pandas operations.

Yes, the tool supports cloud I/O for reading and writing bioinformatics file formats directly from S3, GCS, and Azure. This enables efficient data handling for large genomic datasets stored in cloud environments.

Yes, you need Python 3.11, 3.12, 3.13, or 3.14 along with the Polars library installed. These specific Python versions are required to execute the high-performance genomic range arithmetic and file handling operations.

Besides overlap, the Skill supports nearest, merge, coverage, complement, and subtract operations on genomic intervals. These operations optimize genomic range arithmetic for large datasets using Polars DataFrames.