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PyClone-VI: scalable inference of clonal population structures using whole genome data Gillis, Sierra; Roth, Andrew J. L.

Abstract

Background: At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data. Results: We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Conclusions: Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi .

Item Metadata

Title
PyClone-VI: scalable inference of clonal population structures using whole genome data
Creator
Gillis, Sierra; Roth, Andrew J. L.
Publisher
BioMed Central
Date Issued
2020-12-10
Description
Background: At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data. Results: We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Conclusions: Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi .
Subject
Cancer; Tumour heterogeneity; Cancer evolution; Bayesian statistics
Genre
Article
Type
Text
Language
eng
Date Available
2020-12-11
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0395240
URI
http://hdl.handle.net/2429/76749
Affiliation
Medicine, Faculty of; Science, Faculty of; Non UBC; Computer Science, Department of; Pathology and Laboratory Medicine, Department of
Citation
BMC Bioinformatics. 2020 Dec 10;21(1):571
Publisher DOI
10.1186/s12859-020-03919-2
Peer Review Status
Reviewed
Scholarly Level
Faculty
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International (CC BY 4.0)