Monovar: single nucleotide variant detection in single cells

Current variant callers are not suitable for single-cell DNA sequencing (SCS) as they do not account for allelic dropout, false-positive errors, and coverage non-uniformity. We developed Monovar, a novel statistical method for detecting and genotyping single nucleotide variants in SCS data. Evaluati...

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Detalles Bibliográficos
Autores principales: Zafar, Hamim, Wang, Yong, Nakhleh, Luay, Navin, Nicholas, Chen, Ken
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4887298/
https://www.ncbi.nlm.nih.gov/pubmed/27088313
http://dx.doi.org/10.1038/nmeth.3835
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author Zafar, Hamim
Wang, Yong
Nakhleh, Luay
Navin, Nicholas
Chen, Ken
author_facet Zafar, Hamim
Wang, Yong
Nakhleh, Luay
Navin, Nicholas
Chen, Ken
author_sort Zafar, Hamim
collection PubMed
description Current variant callers are not suitable for single-cell DNA sequencing (SCS) as they do not account for allelic dropout, false-positive errors, and coverage non-uniformity. We developed Monovar, a novel statistical method for detecting and genotyping single nucleotide variants in SCS data. Evaluation based on an isogenic fibroblast cell line and three different human tumor datasets showed substantial improvement of Monovar over standard algorithms for identifying driver mutations and delineating clonal substructure.
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spelling pubmed-48872982016-10-18 Monovar: single nucleotide variant detection in single cells Zafar, Hamim Wang, Yong Nakhleh, Luay Navin, Nicholas Chen, Ken Nat Methods Article Current variant callers are not suitable for single-cell DNA sequencing (SCS) as they do not account for allelic dropout, false-positive errors, and coverage non-uniformity. We developed Monovar, a novel statistical method for detecting and genotyping single nucleotide variants in SCS data. Evaluation based on an isogenic fibroblast cell line and three different human tumor datasets showed substantial improvement of Monovar over standard algorithms for identifying driver mutations and delineating clonal substructure. 2016-04-18 2016-06 /pmc/articles/PMC4887298/ /pubmed/27088313 http://dx.doi.org/10.1038/nmeth.3835 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Zafar, Hamim
Wang, Yong
Nakhleh, Luay
Navin, Nicholas
Chen, Ken
Monovar: single nucleotide variant detection in single cells
title Monovar: single nucleotide variant detection in single cells
title_full Monovar: single nucleotide variant detection in single cells
title_fullStr Monovar: single nucleotide variant detection in single cells
title_full_unstemmed Monovar: single nucleotide variant detection in single cells
title_short Monovar: single nucleotide variant detection in single cells
title_sort monovar: single nucleotide variant detection in single cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4887298/
https://www.ncbi.nlm.nih.gov/pubmed/27088313
http://dx.doi.org/10.1038/nmeth.3835
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AT nakhlehluay monovarsinglenucleotidevariantdetectioninsinglecells
AT navinnicholas monovarsinglenucleotidevariantdetectioninsinglecells
AT chenken monovarsinglenucleotidevariantdetectioninsinglecells

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