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Performance comparison of four exome capture systems for deep sequencing

BACKGROUND: Recent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commerc...

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Autores principales: Chilamakuri, Chandra Sekhar Reddy, Lorenz, Susanne, Madoui, Mohammed-Amin, Vodák, Daniel, Sun, Jinchang, Hovig, Eivind, Myklebost, Ola, Meza-Zepeda, Leonardo A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092227/
https://www.ncbi.nlm.nih.gov/pubmed/24912484
http://dx.doi.org/10.1186/1471-2164-15-449
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author Chilamakuri, Chandra Sekhar Reddy
Lorenz, Susanne
Madoui, Mohammed-Amin
Vodák, Daniel
Sun, Jinchang
Hovig, Eivind
Myklebost, Ola
Meza-Zepeda, Leonardo A
author_facet Chilamakuri, Chandra Sekhar Reddy
Lorenz, Susanne
Madoui, Mohammed-Amin
Vodák, Daniel
Sun, Jinchang
Hovig, Eivind
Myklebost, Ola
Meza-Zepeda, Leonardo A
author_sort Chilamakuri, Chandra Sekhar Reddy
collection PubMed
description BACKGROUND: Recent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commercially available; here we compare the performance of four of them: NimbleGen’s SeqCap EZ v3.0, Agilent’s SureSelect v4.0, Illumina’s TruSeq Exome, and Illumina’s Nextera Exome, all applied to the same human tumor DNA sample. RESULTS: Each capture technology was evaluated for its coverage of different exome databases, target coverage efficiency, GC bias, sensitivity in single nucleotide variant detection, sensitivity in small indel detection, and technical reproducibility. In general, all technologies performed well; however, our data demonstrated small, but consistent differences between the four capture technologies. Illumina technologies cover more bases in coding and untranslated regions. Furthermore, whereas most of the technologies provide reduced coverage in regions with low or high GC content, the Nextera technology tends to bias towards target regions with high GC content. CONCLUSIONS: We show key differences in performance between the four technologies. Our data should help researchers who are planning exome sequencing to select appropriate exome capture technology for their particular application. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-449) contains supplementary material, which is available to authorized users.
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spelling pubmed-40922272014-07-21 Performance comparison of four exome capture systems for deep sequencing Chilamakuri, Chandra Sekhar Reddy Lorenz, Susanne Madoui, Mohammed-Amin Vodák, Daniel Sun, Jinchang Hovig, Eivind Myklebost, Ola Meza-Zepeda, Leonardo A BMC Genomics Research Article BACKGROUND: Recent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commercially available; here we compare the performance of four of them: NimbleGen’s SeqCap EZ v3.0, Agilent’s SureSelect v4.0, Illumina’s TruSeq Exome, and Illumina’s Nextera Exome, all applied to the same human tumor DNA sample. RESULTS: Each capture technology was evaluated for its coverage of different exome databases, target coverage efficiency, GC bias, sensitivity in single nucleotide variant detection, sensitivity in small indel detection, and technical reproducibility. In general, all technologies performed well; however, our data demonstrated small, but consistent differences between the four capture technologies. Illumina technologies cover more bases in coding and untranslated regions. Furthermore, whereas most of the technologies provide reduced coverage in regions with low or high GC content, the Nextera technology tends to bias towards target regions with high GC content. CONCLUSIONS: We show key differences in performance between the four technologies. Our data should help researchers who are planning exome sequencing to select appropriate exome capture technology for their particular application. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-449) contains supplementary material, which is available to authorized users. BioMed Central 2014-06-09 /pmc/articles/PMC4092227/ /pubmed/24912484 http://dx.doi.org/10.1186/1471-2164-15-449 Text en © Chilamakuri et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 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 Research Article
Chilamakuri, Chandra Sekhar Reddy
Lorenz, Susanne
Madoui, Mohammed-Amin
Vodák, Daniel
Sun, Jinchang
Hovig, Eivind
Myklebost, Ola
Meza-Zepeda, Leonardo A
Performance comparison of four exome capture systems for deep sequencing
title Performance comparison of four exome capture systems for deep sequencing
title_full Performance comparison of four exome capture systems for deep sequencing
title_fullStr Performance comparison of four exome capture systems for deep sequencing
title_full_unstemmed Performance comparison of four exome capture systems for deep sequencing
title_short Performance comparison of four exome capture systems for deep sequencing
title_sort performance comparison of four exome capture systems for deep sequencing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092227/
https://www.ncbi.nlm.nih.gov/pubmed/24912484
http://dx.doi.org/10.1186/1471-2164-15-449
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