polars-bio

Use this skill for genomic interval arithmetic and bioinformatics file I/O through Polars and DataFusion. It targets polars-bio 0.36.0, tested with Polars 1.44.2 on Python 3.13. The upstream package requires Polars >=1.37.1, PyArrow >=23.0.1,<25, DataFusion >=53,<54 and polars-config-meta >=0.3.2,<1. Keep this environment separate from packages needing incompatible Arrow or DataFusion releases.

uv pip install "polars-bio==0.36.0" "polars==1.44.2"
# Optional pandas interoperability (requires pandas >=3):
uv pip install "polars-bio[pandas]==0.36.0" "polars==1.44.2"

Verify new releases against the official release notes and package requirements. The examples below with named files are templates: substitute actual files and check their schemas. The synthetic interval example and small local format round trips were executed during this review.

Workflow

1. Record the assembly, contig naming, coordinate convention, strand policy and unit of analysis. Identical contig names do not prove identical assemblies.
2. Choose readers by format; BCF has its own reader. Inspect schemas and source metadata before selecting attributes or genotypes.
3. Normalize all inputs to one coordinate system and validate their bounds.
4. Choose pair output, hit counts, covered bases or read depth deliberately.
5. Filter and project lazily, then inspect a small result before scaling up.
6. Validate output counts and boundaries against a hand-computable fixture; preserve IDs, coordinate metadata and provenance when saving results.

Coordinate contract

The default is 1-based closed, including converted BED reads. Use use_zero_based=True on genomic readers for 0-based half-open output. This argument converts positions; it is not only a metadata label. For example, BED [0,10) becomes [1,10] by default and remains [0,10) with the override. SAM text POS is 1-based, whereas BAM stores its alignment position internally as 0-based. Both readers expose the requested output convention.

For manually constructed DataFrames, metadata labels existing numbers and does not convert them. Converting closed [s,e] to half-open means s-1,e. Set metadata only after conversion. Never convert twice.

import polars as pl
import polars_bio as pb

pb.set_option("datafusion.bio.coordinate_system_zero_based", True)
pb.set_option("datafusion.bio.coordinate_system_check", True)

query = pl.DataFrame({
    "query_id": ["q1", "q2", "q3"],
    "chrom": ["chr1", "chr1", "chr2"],
    "start": [0, 10, 0], "end": [10, 20, 10],
})
target = pl.DataFrame({
    "chrom": ["chr1", "chr1"], "start": [5, 8], "end": [12, 15],
})
for frame in (query, target):
    frame.config_meta.set(coordinate_system_zero_based=True)

pairs = pb.overlap(query, target).collect()
counts = pb.count_overlaps(query, target).collect().sort("query_id")
covered = pb.coverage(query, target).collect().sort("query_id")
assert pairs.height == 4
assert counts["count"].to_list() == [2, 2, 0]
assert covered["coverage"].to_list() == [5, 5, 0]

Require non-null contigs, integer positions and valid positive-length intervals (0 <= start < end in half-open form), within the chosen assembly. Do not silently turn points/insertions into nonempty intervals: choose the biological convention. Mismatched input metadata raises CoordinateSystemMismatchError; missing metadata warns and uses the global setting by default, or raises MissingCoordinateSystemError in strict mode. Inspect pb.get_metadata(frame) after transformations and SQL. See configuration.

Choose the operation

…