Monovar: single-nucleotide variant detection in single cells.
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ABSTRACT:
SUBMITTER: Zafar H
PROVIDER: S-EPMC4887298 | biostudies-literature | 2016 Jun
REPOSITORIES: biostudies-literature
Publications
Monovar: single-nucleotide variant detection in single cells.
Zafar Hamim H Wang Yong Y Nakhleh Luay L Navin Nicholas N Chen Ken K
Nature methods 20160418 6
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 h ...[more]
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