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dc.contributor.authorCaravagna, G
dc.contributor.authorSanguinetti, G
dc.contributor.authorGraham, TA
dc.contributor.authorSottoriva, A
dc.date.accessioned2020-11-23T13:15:47Z
dc.date.issued2020-11-17
dc.identifier.citationBMC bioinformatics, 2020, 21 (1), pp. 531 - ?
dc.identifier.issn1471-2105
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4240
dc.identifier.eissn1471-2105
dc.identifier.doi10.1186/s12859-020-03863-1
dc.description.abstractBACKGROUND: 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.
dc.formatElectronic
dc.format.extent531 - ?
dc.languageeng
dc.language.isoeng
dc.publisherBMC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectClone Cells
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectDNA, Neoplasm
dc.subjectGenetics, Population
dc.subjectCell Proliferation
dc.subjectMutation
dc.subjectModels, Genetic
dc.subjectSoftware
dc.subjectFemale
dc.subjectMachine Learning
dc.subjectWhole Genome Sequencing
dc.subjectData Analysis
dc.titleThe MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data.
dc.typeJournal Article
dcterms.dateAccepted2020-11-04
rioxxterms.versionofrecord10.1186/s12859-020-03863-1
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-11-17
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBMC bioinformatics
pubs.issue1
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR
pubs.publication-statusPublished
pubs.volume21
pubs.embargo.termsNot known
dc.contributor.icrauthorGraham, Trevor
dc.contributor.icrauthorSottoriva, Andrea


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