Journal
NATURE METHODS
Volume 13, Issue 6, Pages 505-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.3835
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Funding
- Lefkofsky Foundation
- NCI [R01CA169244-01, R01CA172652, CA016672]
- NIH [R21CA174397]
- Agilent University Relations
- MD Anderson Knowledge Gap and Center for Genetics Genomics
- Bosarge Foundation
- Chapman Foundation
- Dell Foundation
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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.
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