Zafar, HamimWang, YongNakhleh, LuayNavin, NicholasChen, Ken2016-11-102016-11-102016Zafar, 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.https://hdl.handle.net/1911/92701Current 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.engThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer Nature.Journal articlehttp://dx.doi.org/10.1038/nmeth.3835