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SNES: single nucleus exome sequencing

Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nucl...

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Detalles Bibliográficos
Autores principales: Leung, Marco L, Wang, Yong, Waters, Jill, Navin, Nicholas E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373516/
https://www.ncbi.nlm.nih.gov/pubmed/25853327
http://dx.doi.org/10.1186/s13059-015-0616-2
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author Leung, Marco L
Wang, Yong
Waters, Jill
Navin, Nicholas E
author_facet Leung, Marco L
Wang, Yong
Waters, Jill
Navin, Nicholas E
author_sort Leung, Marco L
collection PubMed
description Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nuclei, time-limited multiple-displacement-amplification, exome capture, and next-generation sequencing to generate high coverage (96%) data from single human cells. We validated our method in a fibroblast cell line, and show low allelic dropout and false-positive error rates, resulting in high detection efficiencies for single nucleotide variants (92%) and indels (85%) in single cells. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0616-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-43735162015-03-26 SNES: single nucleus exome sequencing Leung, Marco L Wang, Yong Waters, Jill Navin, Nicholas E Genome Biol Method Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nuclei, time-limited multiple-displacement-amplification, exome capture, and next-generation sequencing to generate high coverage (96%) data from single human cells. We validated our method in a fibroblast cell line, and show low allelic dropout and false-positive error rates, resulting in high detection efficiencies for single nucleotide variants (92%) and indels (85%) in single cells. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0616-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-03-25 2015 /pmc/articles/PMC4373516/ /pubmed/25853327 http://dx.doi.org/10.1186/s13059-015-0616-2 Text en © Leung et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Leung, Marco L
Wang, Yong
Waters, Jill
Navin, Nicholas E
SNES: single nucleus exome sequencing
title SNES: single nucleus exome sequencing
title_full SNES: single nucleus exome sequencing
title_fullStr SNES: single nucleus exome sequencing
title_full_unstemmed SNES: single nucleus exome sequencing
title_short SNES: single nucleus exome sequencing
title_sort snes: single nucleus exome sequencing
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373516/
https://www.ncbi.nlm.nih.gov/pubmed/25853327
http://dx.doi.org/10.1186/s13059-015-0616-2
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