GenomicRanges 0.8.4

Container class to represent and operate over genomic regions and annotations.

GenomicRanges

GenomicRanges provides container classes designed to represent genomic locations and support genomic analysis. It is similar to Bioconductor's GenomicRanges.

To get started, install the package from PyPI

pip install genomicranges

Some of the methods like read_ucsc require optional packages to be installed, e.g. joblib and can be installed by:

pip install genomicranges[optional]

GenomicRanges

GenomicRanges is the base class to represent and operate over genomic regions and annotations.

From Bioinformatic file formats

[!NOTE] When reading genomic formats, ends are expected to be inclusive to be consistent with Bioconductor representations (& gff). If they are not, we recommend subtracting 1 from the ends.

From biobear

Although the parsing capabilities in this package are limited, the biobear library is designed for reading and searching various bioinformatics file formats, including FASTA, FASTQ, VCF, BAM, and GFF, or from an object store like S3. Users can esily convert these representations to GenomicRanges (or read more here):

from genomicranges import GenomicRanges
import biobear as bb

session = bb.new_session()

df = session.read_gtf_file("path/to/test.gtf").to_polars()
df = df.rename({"seqname": "seqnames", "start": "starts", "end": "ends"})

gg = GenomicRanges.from_polars(df)

# do stuff w/ a genomic ranges
print(len(gg), len(df))

## output
## 77 77> [!NOTE]

ends are expected to be inclusive to be consistent with Bioconductor representations. If they are not, we recommend subtracting 1 from the ends.

UCSC or GTF file

You can easily download and parse genome annotations from UCSC or load a genome annotation from a GTF file,

import genomicranges

gr = genomicranges.read_gtf(<PATH TO GTF>)
# OR
gr = genomicranges.read_ucsc(genome="hg19")

print(gr)

## output
## GenomicRanges with 1760959 intervals & 10 metadata columns.
## ... truncating the console print ...

From IRanges (Preferred way)

If you have all relevant information to create a GenomicRanges object

from genomicranges import GenomicRanges
from iranges import IRanges
from biocframe import BiocFrame
from random import random

gr = GenomicRanges(
    seqnames=[
        "chr1",
        "chr2",
        "chr3",
        "chr2",
        "chr3",
    ],
    ranges=IRanges(start=[x for x in range(101, 106)], width=[11, 21, 25, 30, 5]),
    strand=["*", "-", "*", "+", "-"],
    mcols=BiocFrame(
        {
            "score": range(0, 5),
            "GC": [random() for _ in range(5)],
        }
    ),
)

print(gr)

## output
GenomicRanges with 5 ranges and 5 metadata columns
    seqnames    ranges           strand     score                  GC
       <str> <IRanges> <ndarray[int64]>   <range>              <list>
[0]     chr1 101 - 111                * |       0  0.2593301003406461
[1]     chr2 102 - 122                - |       1  0.7207993213776644
[2]     chr3 103 - 127                * |       2 0.23391468067222065
[3]     chr2 104 - 133                + |       3  0.7671026589720187
[4]     chr3 105 - 109                - |       4 0.03355777784472458
------
seqinfo(3 sequences): chr1 chr2 chr3

Pandas DataFrame

A common representation in Python is a pandas DataFrame for all tabular datasets. DataFrame must contain columns "seqnames", "starts", and "ends" to represent genomic intervals. Here's an example:

from genomicranges import GenomicRanges
import pandas as pd
from random import random

df = pd.DataFrame(
    {
        "seqnames": ["chr1", "chr2", "chr1", "chr3", "chr2"],
        "starts": [101, 102, 103, 104, 109],
        "ends": [112, 103, 128, 134, 111],
        "strand": ["*", "-", "*", "+", "-"],
        "score": range(0, 5),
        "GC": [random() for _ in range(5)],
    }
)

gr = GenomicRanges.from_pandas(df)
print(gr)

## output
GenomicRanges with 5 ranges and 5 metadata columns
  seqnames    ranges           strand    score                  GC
     <str> <IRanges> <ndarray[int64]>   <list>              <list>
0     chr1 101 - 111                * |      0  0.4862658925128007
1     chr2 102 - 102                - |      1 0.27948386889389953
2     chr1 103 - 127                * |      2  0.5162697718607901
3     chr3 104 - 133                + |      3  0.5979843806415466
4     chr2 109 - 110                - |      4 0.04740781186083798
------
seqinfo(3 sequences): chr1 chr2 chr3

Polars DataFrame

Similarly, To initialize from a polars DataFrame:

from genomicranges import GenomicRanges
import polars as pl
from random import random

df = pl.DataFrame(
    {
        "seqnames": ["chr1", "chr2", "chr1", "chr3", "chr2"],
        "starts": [101, 102, 103, 104, 109],
        "ends": [112, 103, 128, 134, 111],
        "strand": ["*", "-", "*", "+", "-"],
        "score": range(0, 5),
        "GC": [random() for _ in range(5)],
    }
)

gr = GenomicRanges.from_polars(df)
print(gr)

## output
GenomicRanges with 5 ranges and 5 metadata columns
  seqnames    ranges           strand    score                  GC
     <str> <IRanges> <ndarray[int64]>   <list>              <list>
0     chr1 101 - 112                * |      0  0.4862658925128007
1     chr2 102 - 103                - |      1 0.27948386889389953
2     chr1 103 - 128                * |      2  0.5162697718607901
3     chr3 104 - 134                + |      3  0.5979843806415466
4     chr2 109 - 111                - |      4 0.04740781186083798
------
seqinfo(3 sequences): chr1 chr2 chr3

Interval Operations

GenomicRanges supports most interval based operations.

subject = genomicranges.read_ucsc(genome="hg38")

query = genomicranges.from_pandas(
    pd.DataFrame(
        {
            "seqnames": ["chr1", "chr2", "chr3"],
            "starts": [100, 115, 119],
            "ends": [103, 116, 120],
        }
    )
)

hits = subject.nearest(query, ignore_strand=True, select="all")
print(hits)

## output
BiocFrame with 3 rows and 2 columns
        query_hits        self_hits
    <ndarray[int32]> <ndarray[int32]>
[0]                0                0
[1]                1          1677082
[2]                2          1003411

CompressedGenomicRangesList

Just as it sounds, a CompressedGenomicRangesList is a named-list like object. If you are wondering why you need this class, a GenomicRanges object lets us specify multiple genomic elements, usually where the genes start and end. Genes are themselves made of many sub-regions, e.g. exons. CompressedGenomicRangesList allows us to represent this nested structure.

Currently, this class is limited in functionality.

To construct a CompressedGenomicRangesList

from genomicranges import GenomicRanges, CompressedGenomicRangesList
from iranges import IRanges
from biocframe import BiocFrame

gr1 = GenomicRanges(
    seqnames=["chr1", "chr2", "chr1", "chr3"],
    ranges=IRanges([1, 3, 2, 4], [10, 30, 50, 60]),
    strand=["-", "+", "*", "+"],
    mcols=BiocFrame({"score": [1, 2, 3, 4]}),
)

gr2 = GenomicRanges(
    seqnames=["chr2", "chr4", "chr5"],
    ranges=IRanges([3, 6, 4], [30, 50, 60]),
    strand=["-", "+", "*"],
    mcols=BiocFrame({"score": [2, 3, 4]}),
)
grl = CompressedGenomicRangesList.from_list(lst=[gr1, gr2], names=["gene1", "gene2"])
print(grl)

## output
CompressedGenomicRangesList with 2 ranges and 2 metadata columns

Name: gene1
GenomicRanges with 4 ranges and 4 metadata columns
    seqnames    ranges           strand    score
       <str> <IRanges> <ndarray[int64]>   <list>
[0]     chr1    1 - 10                - |      1
[1]     chr2    3 - 32                + |      2
[2]     chr1    2 - 51                * |      3
[3]     chr3    4 - 63                + |      4
------
seqinfo(3 sequences): chr1 chr2 chr3

Name: gene2
GenomicRanges with 3 ranges and 3 metadata columns
    seqnames    ranges           strand    score
       <str> <IRanges> <ndarray[int64]>   <list>
[0]     chr2    3 - 32                - |      2
[1]     chr4    6 - 55                + |      3
[2]     chr5    4 - 63                * |      4
------
seqinfo(3 sequences): chr2 chr4 chr5

Performance

Performance comparison between Python and R GenomicRanges implementations. The query dataset contains approximately 564,000 intervals, while the subject dataset contains approximately 71 million intervals.

Operation	Python/GenomicRanges	Python/GenomicRanges (5 threads)	R/GenomicRanges
Overlap	2.80s	2.06s	4.40s
Overlap (single chromosome)	6.73s	5.19s	10.06s
Nearest	2.27s	1.5s	42.16s
Nearest (single chromosome)	4.7s	4.67s	11.01s

[!NOTE] The single chromosome benchmark ignores chromosome/sequence information and performs overlap operations solely on intervals.

For details, see the scripts in the benchmark directory.

Further information

• Tutorial
• API documentation
• Bioc/GenomicRanges

Note

This project has been set up using PyScaffold 4.1.1. For details and usage information on PyScaffold see https://pyscaffold.org/.