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Monovar: single-nucleotide variant detection in single cells.

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.

SUBMITTER: Zafar H 

PROVIDER: S-EPMC4887298 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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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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