Skip to main content

Genetics with Numpy

Project description

Tests codecov Docs PyPI version

gumpy

Genetics with Numpy

Installation

git clone https://github.com/oxfordmmm/gumpy
cd gumpy
pip install .

Documentation

https://oxfordmmm.github.io/gumpy/

Testing

A suite of tests can be run from a terminal:

python -m pytest --cov=gumpy -vv

Usage

Parse a genbank file

Genome objects can be created by passing a filename of a genbank file

from gumpy import Genome

g = Genome("filename.gbk")

Parse a VCF file

VCFFile objects can be created by passing a filename of a vcf file

from gumpy import VCFFile

vcf = VCFFile("filename.vcf")

Apply a VCF file to a reference genome

The mutations defined in a vcf file can be applied to a reference genome to produce a new Genome object containing the changes detailed in the vcf.

If a contig is set within the vcf, the length of the contig should match the length of the genome. Otherwise, if the vcf details changes within the genome range, they will be made.

from gumpy import Genome, VCFFile

reference_genome = Genome("reference.gbk")
vcf = VCFFile("filename.vcf")

resultant_genome = reference_genome + vcf

Genome level comparisons

There are two different methods for comparing changes. One can quickly check for changes which are caused by a given VCF file. The other can check for changes between two genome. The latter is therefore suited best for comparisons in which either both genomes are mutated, or the VCF file(s) are not available. The former is best suited for cases where changes caused by a VCF want to be determined, but finding gene-level differences will require rebuilding the Gene objects, which can be time consuming.

Compare genomes

Two genomes of the same length can be easily compared, including equality and changes between the two. Best suited to cases where two mutated genomes are to be compared.

from gumpy import Genome, GenomeDifference

g1 = Genome("filename1.gbk")
g2 = Genome("filename2.gbk")

diff = g2 - g1 #Genome.difference returns a GenomeDifference object
print(diff.snp_distance) #SNP distance between the two genomes
print(diff.variants) #Array of variants (SNPs/INDELs) of the differences between g2 and g1

Gene level comparisons

When a Genome object is instanciated, it is populated with Gene objects for each gene detailed in the genbank file. These genes can also be compared. Gene differences can be found through direct comparison of Gene objects, or systematically through the gene_differences() method of GenomeDifference.

from gumpy import Genome, Gene

g1 = Genome("filename1.gbk")
g2 = Genome("filename2.gbk")

#Get the Gene objects for the gene "gene1_name" from both Genomes
g1_gene1 = g1.build_gene["gene1_name"]
g2_gene1 = g2.build_gene["gene1_name"]

g1_gene1 == g2_gene1 #Equality check of the two genes
diff= g1_gene1 - g2_gene1 #Returns a GeneDifference object
diff.mutations #List of mutations in GARC describing the variation between the two genes

Save and load Genome objects

Due to how long it takes to create a Genome object, it may be beneficial to save the object to disk. The reccomendation is to utilise the pickle module to do so, but due to the security implications of this, do so at your own risk! An example is below:

import pickle

import gumpy

#Load genome
g = gumpy.Genome("filename.gbk")

#Save genome
pickle.dump(g, open("filename.pkl", "wb"))

#Load genome
g2 = pickle.load(open("filename.pkl", "rb"))

g == g2 #True

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gumpy-1.2.3.tar.gz (44.5 kB view details)

Uploaded Source

Built Distribution

gumpy-1.2.3-py3-none-any.whl (46.2 kB view details)

Uploaded Python 3

File details

Details for the file gumpy-1.2.3.tar.gz.

File metadata

  • Download URL: gumpy-1.2.3.tar.gz
  • Upload date:
  • Size: 44.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for gumpy-1.2.3.tar.gz
Algorithm Hash digest
SHA256 8754ce977fe324d39f23f002f273d09837c064fb9253b28eee5fd1fa8d5535dc
MD5 81a6350ee15c8e7f54147c52bc44c401
BLAKE2b-256 2d28d6a5423e1b6662c6e424b73c1eddee5783fe66ae95ca492828bab93355a8

See more details on using hashes here.

File details

Details for the file gumpy-1.2.3-py3-none-any.whl.

File metadata

  • Download URL: gumpy-1.2.3-py3-none-any.whl
  • Upload date:
  • Size: 46.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for gumpy-1.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 5111ee7cf48616375f94e4de060bc4d104fe82303a74499bae9de109ac7f8994
MD5 35ef11c6c4393fb76b59ffbd9484387e
BLAKE2b-256 7c79b8b74ff31ee1c5822a5639736365bdb57b8ceb8a714b247de2ca36e269ce

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page