TY - JOUR
T1 - CNVpytor
T2 - a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing
AU - Suvakov, Milovan
AU - Panda, Arijit
AU - Diesh, Colin
AU - Holmes, Ian
AU - Abyzov, Alexej
N1 - Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press GigaScience.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Background: Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Findings: Herein, we present an extension of CNVnator developed in Python - CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number-neutral losses of heterozygosity. Conclusions: CNVpytor is significantly faster than CNVnator - particularly for parsing alignment files (2-20 times faster) - and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.
AB - Background: Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Findings: Herein, we present an extension of CNVnator developed in Python - CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number-neutral losses of heterozygosity. Conclusions: CNVpytor is significantly faster than CNVnator - particularly for parsing alignment files (2-20 times faster) - and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.
KW - Python
KW - copy number alternations
KW - copy number variations
KW - whole-genome sequencing
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U2 - 10.1093/gigascience/giab074
DO - 10.1093/gigascience/giab074
M3 - Article
C2 - 34817058
AN - SCOPUS:85121106072
SN - 2047-217X
VL - 10
JO - GigaScience
JF - GigaScience
IS - 11
M1 - giab074
ER -