polars-bio

Genomic interval operations like overlap and nearest are performed directly on Polars DataFrames using the functional API or LazyFrame .pb accessor, materializing results via collect. You call pb.overlap(df1, df2).collect() to execute the interval math.

Yes, you can register bioinformatics files such as BED, VCF, and BAM as DataFusion tables to query them with SQL. This supports scalable streaming workflows, allowing you to pipe SQL query results directly into downstream interval operations.

Handling large cohort genomic interval matching is best achieved using scalable, out-of-core execution on Polars LazyFrames. This approach streams large datasets via DataFusion, enabling high-throughput overlap, coverage, and subtract operations without memory constraints.

Yes, genomic interval math requires Polars-backed interval schemas with chrom, start, and end columns by default. Adhering to this specific schema structure ensures that operations like merge, complement, and subtract execute correctly on your interval data.

Migrating from bioframe to a Polars-backed alternative accelerates genomic interval operations by leveraging out-of-core execution and DataFusion streaming. This provides faster processing for high-throughput tasks like overlap and coverage on large bioinformatics datasets.

A key limitation is that streaming genomic interval operations require strict Polars-backed interval schemas with chrom, start, and end columns. You must also use collect or eager output_type for output materialization, which impacts memory usage during large-scale analytics.