adagenes 0.1.4

Generic toolkit for processing DNA polymorphism data

AdaGenes

AdaGenes is a generic toolkit for processing, annotating, filtering and transforming DNA polymorphism data.

Main features:

• A powerful data object to store and edit DNA mutation data
• Functionality to read and write files in common genomics file formats, including VCF, MAF, CSV/TSV, XLSX and plain text files
• Effective variant filtering according to specific threshold or feature values
• Liftover genome positions between hg38/GRCh38, hg19/GRCh37 and T2T-CHM13 reference genomes
• Effective variant normalization in VCF and HGVS notation

Installation

pip install adagenes

Getting started

Read files

Start by reading in a data file in one of the supported file formats in a biomarker frame with the read_file() function. adagenes automatically identifies the file type and inititates the corresponding file reader. You may also manually inititate a file reader and call its read_file() function:

import adagenes as av

bframe = av.read_file("data/somaticMutations.vcf")

# Print biomarker identifiers
print(bframe.get_ids())

# Print loaded variant data completely
print(bframe.data)

If the variant data has been parsed correctly, the data of the biomarker frame should be a nested JSON dictionary:

{
'chr7:140753336A>T': {'variant_data': {'CHROM': '7', 'POS': '140753336', 'ID': '.', 'REF': 'A', 'ALT': 'T', 'QUAL': '100', ... },
'chr1:2556664C>.': {'variant_data': {'CHROM': '1', 'POS': '2556664', 'ID': '.', ... } }
}

Variant notations and normalization

Filter mutations

Liftover

Convert the genomic positions of variants between genome assemblies with the liftover function (GRCh37 / GRCh38 / T2T-CHM13):

import adagenes as ag

# Load a biomarker frame by defining the genome version (hg19/hg38/t2t)
infile = "somaticMutations.vcf"
bframe = ag.read_file(infile, genome_version="hg38")

# Liftover to another genome assemly
bframe_t2t = ag.liftover(bframe, target_genome="t2t")

# Write the new biomarker frame in T2T to a file
ag.write_file("somaticMutations.t2t.vcf", bframe_t2t)

Visualization

Annotate variants

You can easily annotate variant data by combining an AdaGenes biomarker frame with the Onkopus annotation framework:

pip install onkopus

Annotate the variant data of a biomarker frame by calling an Onkopus client directly on the bframe.data:

import adageness as av
import onkopus as op

genome_version="hg38"
bframe = av.read_file("somaticMutations.vcf", genome_version="hg38")

# Annotate with all Onkopus modules
bframe.data = op.annotate(bframe.data)

# Annotate with specific modules
bframe.data = op.AlphaMissenseClient(genome_version=genome_version).process_data(bframe.data)
bframe.data = op.GENCODEClient(genome_version=genome_version).process_data(bframe.data)

av.write_file("somaticMutations.annotated.avf",bframe)

Save data

Write a biomarker frame to a file with write_file() in one of the supported file formats (.vcf,.maf,.csv):

import adagenes as av

av.write_file("/data/somaticMutations.annotated.maf", bframe, file_type="csv")

Dependencies

• scikit-learn
• pandas
• matplotlib
• plotly
• pyliftover
• blosum
• openpyxl
• requests

License

GPLv3

Documentation