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Nature Research, Nature Methods, 6(13), p. 505-507, 2016

DOI: 10.1038/nmeth.3835

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Monovar: single-nucleotide variant detection in single cells

Journal article published in 2016 by Hamim Zafar, Yong Wang ORCID, Luay Nakhleh, Nicholas Navin, Ken Chen
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Current 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.