GenomicRanges¶
GenomicRanges provides container classes designed to represent genomic locations and support genomic analysis. It is similar to Bioconductor’s GenomicRanges.
Note: V0.4.0 is a complete overhaul of the package, as such the constructor to GenomicRanges has changed. Please refer the documentation for updated usage of the classes and the methods.
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¶
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
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 - 112 * | 0 0.2593301003406461
[1] chr2 102 - 123 - | 1 0.7207993213776644
[2] chr3 103 - 128 * | 2 0.23391468067222065
[3] chr2 104 - 134 + | 3 0.7671026589720187
[4] chr3 105 - 110 - | 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 - 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
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)
print(hits)
## output
[[0, 1], [1677082, 1677083, 1677084], [1003411, 1003412]]
GenomicRangesList¶
Just as it sounds, a GenomicRangesList 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. GenomicRangesList allows us to represent this nested structure.
Currently, this class is limited in functionality.
To construct a GenomicRangesList
from genomicranges import GenomicRanges, GenomicRangesList
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 = GenomicRangesList(ranges=[gr1, gr2], names=["gene1", "gene2"])
print(grl)
## output
GenomicRangesList 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 - 11 - | 1
[1] chr2 3 - 33 + | 2
[2] chr1 2 - 52 * | 3
[3] chr3 4 - 64 + | 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 - 33 - | 2
[1] chr4 6 - 56 + | 3
[2] chr5 4 - 64 * | 4
------
seqinfo(3 sequences): chr2 chr4 chr5
Further information¶
Note¶
This project has been set up using PyScaffold 4.1.1. For details and usage information on PyScaffold see https://pyscaffold.org/.