Monovar: single-nucleotide variant detection in single cells

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dc.citation.firstpage505en_US
dc.citation.journalTitleNature Methodsen_US
dc.citation.lastpage507en_US
dc.citation.volumeNumber13en_US
dc.contributor.authorZafar, Hamimen_US
dc.contributor.authorWang, Yongen_US
dc.contributor.authorNakhleh, Luayen_US
dc.contributor.authorNavin, Nicholasen_US
dc.contributor.authorChen, Kenen_US
dc.date.accessioned2016-11-10T22:23:40Zen_US
dc.date.available2016-11-10T22:23:40Zen_US
dc.date.issued2016en_US
dc.description.abstractCurrent variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.en_US
dc.identifier.citationZafar, Hamim, Wang, Yong, Nakhleh, Luay, et al.. "Monovar: single-nucleotide variant detection in single cells." <i>Nature Methods,</i> 13, (2016) Springer Nature: 505-507. http://dx.doi.org/10.1038/nmeth.3835.en_US
dc.identifier.doihttp://dx.doi.org/10.1038/nmeth.3835en_US
dc.identifier.urihttps://hdl.handle.net/1911/92701en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer Nature.en_US
dc.titleMonovar: single-nucleotide variant detection in single cellsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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