Monovar: Single-nucleotide variant detection in single cells

Hamim Zafar; Yong Wang; Luay Nakhleh; Nicholas Navin; Ken Chen

doi:10.1038/nmeth.3835

Monovar: Single-nucleotide variant detection in single cells

Hamim Zafar, Yong Wang, Luay Nakhleh, Nicholas Navin, Ken Chen

Research output: Contribution to journal › Article › peer-review

107 Scopus citations

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.

Original language	English (US)
Pages (from-to)	505-507
Number of pages	3
Journal	Nature Methods
Volume	13
Issue number	6
DOIs	https://doi.org/10.1038/nmeth.3835
State	Published - Jun 1 2016

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

MD Anderson CCSG core facilities

Advanced Technology Genomics Core
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10.1038/nmeth.3835

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Monovar: Single-nucleotide variant detection in single cells

Abstract

ASJC Scopus subject areas

MD Anderson CCSG core facilities

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