🔨Features

Features

Genomic ranges operations

Features	Bioframe	polars-bio	PyRanges	Pybedtools	PyGenomics	GenomicRanges
overlap
nearest
count_overlaps
cluster
merge
complement
coverage
expand
sort
read_table

API comparison between libraries

There is no standard API for genomic ranges operations in Python. This table compares the API of the libraries. The table is not exhaustive and only shows the most common operations used in benchmarking.

operation	Bioframe	polars-bio	PyRanges0	PyRanges1	Pybedtools	GenomicRanges
overlap	overlap	overlap	join¹	join_ranges	intersect²	find_overlaps³
nearest	closest	nearest	nearest	nearest	closest⁴	nearest⁵
read_table	read_table	read_table	read_bed	read_bed	BedTool	read_bed

Note

There is an overlap method in PyRanges, but its output is only limited to indices of intervals from the other Dataframe that overlap. In Bioframe's benchmark also join method instead of overlap was used.
wa and wb options used to obtain a comparable output.
Output contains only a list with the same length as query, containing hits to overlapping indices. Data transformation is required to obtain the same output as in other libraries. Since the performance was far worse than in more efficient libraries anyway, additional data transformation was not included in the benchmark.
s=first was used to obtain a comparable output.
select="arbitrary" was used to obtain a comparable output.

File formats support

Format	Support level
BED
VCF
BAM
FASTQ
FASTA
GFF
GTF
Indexed VCF
Indexed BAM

SQL-powered data processing

polars-bio provides a SQL-like API for bioinformatic data querying or manipulation. Check SQL reference for more details.

import polars_bio as pb
pb.register_vcf("gs://gcp-public-data--gnomad/release/4.1/genome_sv/gnomad.v4.1.sv.sites.vcf.gz", "gnomad_sv", thread_num=1, info_fields=["SVTYPE", "SVLEN"])
pb.sql("SELECT * FROM gnomad_sv WHERE SVTYPE = 'DEL' AND SVLEN > 1000").limit(3).collect()

shape: (3, 10)
┌───────┬───────┬───────┬────────────────────────────────┬───┬───────┬────────────┬────────┬───────┐
│ chrom ┆ start ┆ end   ┆ id                             ┆ … ┆ qual  ┆ filter     ┆ svtype ┆ svlen │
│ ---   ┆ ---   ┆ ---   ┆ ---                            ┆   ┆ ---   ┆ ---        ┆ ---    ┆ ---   │
│ str   ┆ u32   ┆ u32   ┆ str                            ┆   ┆ f64   ┆ str        ┆ str    ┆ i32   │
╞═══════╪═══════╪═══════╪════════════════════════════════╪═══╪═══════╪════════════╪════════╪═══════╡
│ chr1  ┆ 22000 ┆ 30000 ┆ gnomAD-SV_v3_DEL_chr1_fa103016 ┆ … ┆ 999.0 ┆ HIGH_NCR   ┆ DEL    ┆ 8000  │
│ chr1  ┆ 40000 ┆ 47000 ┆ gnomAD-SV_v3_DEL_chr1_b26f63f7 ┆ … ┆ 145.0 ┆ PASS       ┆ DEL    ┆ 7000  │
│ chr1  ┆ 79086 ┆ 88118 ┆ gnomAD-SV_v3_DEL_chr1_733c4ef0 ┆ … ┆ 344.0 ┆ UNRESOLVED ┆ DEL    ┆ 9032  │
└───────┴───────┴───────┴────────────────────────────────┴───┴───────┴────────────┴────────┴───────┘

Parallel engine and streaming processing 🏎️

It is straightforward to parallelize operations in polars-bio. The library is built on top of Apache DataFusion you can set the degree of parallelism using the datafusion.execution.target_partitions option, e.g.:

import polars_bio as pb
pb.set_option("datafusion.execution.target_partitions", "8")

Tip

The default value is 1 (parallel execution disabled).
The datafusion.execution.target_partitions option is a global setting and affects all operations in the current session.
Check available strategies for optimal performance.
See the other configuration settings in the Apache DataFusion documentation.

Streaming (out-of-core processing) [Exeprimental]🧪

polars-bio supports out-of-core processing with Polars LazyFrame streaming option. It can bring significant speedup as well reduction in memory usage allowing to process large datasets that do not fit in memory. See our benchmark results.

import os
import polars_bio as pb
os.environ['BENCH_DATA_ROOT'] = "/Users/mwiewior/research/data/databio"
os.environ['POLARS_MAX_THREADS'] = "1"
os.environ['POLARS_VERBOSE'] = "1"

cols=["contig", "pos_start", "pos_end"]
BENCH_DATA_ROOT = os.getenv('BENCH_DATA_ROOT', '/data/bench_data/databio')
df_1 = f"{BENCH_DATA_ROOT}/exons/*.parquet"
df_2 =  f"{BENCH_DATA_ROOT}/exons/*.parquet"
pb.overlap(df_1, df_2, cols1=cols, cols2=cols, streaming=True).collect(streaming=True).limit()

INFO:polars_bio.operation:Running in streaming mode...
INFO:polars_bio.operation:Running Overlap operation with algorithm Coitrees and 1 thread(s)...
'STREAMING:\n  Anonymous SCAN []\n  PROJECT */6 COLUMNS'

pb.overlap(df_1, df_2, cols1=cols, cols2=cols, streaming=True).collect(streaming=True).limit()

INFO:polars_bio.operation:Running in streaming mode...
INFO:polars_bio.operation:Running Overlap operation with algorithm Coitrees and 1 thread(s)...
RUN STREAMING PIPELINE
[anonymous -> ordered_sink]
shape: (5, 6)
┌──────────┬─────────────┬───────────┬──────────┬─────────────┬───────────┐
│ contig_1 ┆ pos_start_1 ┆ pos_end_1 ┆ contig_2 ┆ pos_start_2 ┆ pos_end_2 │
│ ---      ┆ ---         ┆ ---       ┆ ---      ┆ ---         ┆ ---       │
│ str      ┆ i32         ┆ i32       ┆ str      ┆ i32         ┆ i32       │
╞══════════╪═════════════╪═══════════╪══════════╪═════════════╪═══════════╡
│ chr1     ┆ 11873       ┆ 12227     ┆ chr1     ┆ 11873       ┆ 12227     │
│ chr1     ┆ 12612       ┆ 12721     ┆ chr1     ┆ 12612       ┆ 12721     │
│ chr1     ┆ 13220       ┆ 14409     ┆ chr1     ┆ 13220       ┆ 14409     │
│ chr1     ┆ 14361       ┆ 14829     ┆ chr1     ┆ 13220       ┆ 14409     │
│ chr1     ┆ 13220       ┆ 14409     ┆ chr1     ┆ 14361       ┆ 14829     │
└──────────┴─────────────┴───────────┴──────────┴─────────────┴───────────┘

Limitations

Single threaded.
Because of the bug only Polars sink operations, such as collect, sink_csv or sink_parquet are supported.

DataFrames support

I/O	Bioframe	polars-bio	PyRanges	Pybedtools	PyGenomics	GenomicRanges
Pandas DataFrame
Polars DataFrame
Polars LazyFrame
Native readers