The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data.
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Date
2020-11-17Author
Caravagna, G
Sanguinetti, G
Graham, TA
Sottoriva, A
Type
Journal Article
Metadata
Show full item recordAbstract
BACKGROUND: The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal deconvolution methods are used to determine the composition of cancer subpopulations in the biopsy sample, a fundamental step to determine clonal expansions and their evolutionary trajectories. RESULTS: In a recent work we have developed a new model-based approach to carry out subclonal deconvolution from the site frequency spectrum of somatic mutations. This new method integrates, for the first time, an explicit model for neutral evolutionary forces that participate in clonal expansions; in that work we have also shown that our method improves largely over competing data-driven methods. In this Software paper we present mobster, an open source R package built around our new deconvolution approach, which provides several functions to plot data and fit models, assess their confidence and compute further evolutionary analyses that relate to subclonal deconvolution. CONCLUSIONS: We present the mobster package for tumour subclonal deconvolution from bulk sequencing, the first approach to integrate Machine Learning and Population Genetics which can explicitly model co-existing neutral and positive selection in cancer. We showcase the analysis of two datasets, one simulated and one from a breast cancer patient, and overview all package functionalities.
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Subject
Clone Cells
Humans
Breast Neoplasms
DNA, Neoplasm
Genetics, Population
Cell Proliferation
Mutation
Models, Genetic
Software
Female
Machine Learning
Whole Genome Sequencing
Data Analysis
Language
eng
Date accepted
2020-11-04
License start date
2020-11-17
Citation
BMC bioinformatics, 2020, 21 (1), pp. 531 - ?
Publisher
BMC