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dc.contributor.authorChilamakuri, Chandra S R
dc.contributor.authorLorenz, Susanne
dc.contributor.authorMadoui, Mohammed-Amin
dc.contributor.authorVodák, Daniel
dc.contributor.authorSun, Jinchang
dc.contributor.authorHovig, Eivind
dc.contributor.authorMyklebost, Ola
dc.contributor.authorMeza-Zepeda, Leonardo A
dc.date.accessioned2015-10-20T12:47:32Z
dc.date.available2015-10-20T12:47:32Z
dc.date.issued2014
dc.identifier.citationBMC Genomics. 2014 Jun 09;15(1):449
dc.identifier.urihttp://hdl.handle.net/10852/47378
dc.description.abstractBackground 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.
dc.language.isoeng
dc.rightsChilamakuri et al.; licensee BioMed Central Ltd.
dc.rightsAttribution 2.0 Generic
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/
dc.titlePerformance comparison of four exome capture systems for deep sequencing
dc.typeJournal article
dc.date.updated2015-10-20T12:47:32Z
dc.creator.authorChilamakuri, Chandra S R
dc.creator.authorLorenz, Susanne
dc.creator.authorMadoui, Mohammed-Amin
dc.creator.authorVodák, Daniel
dc.creator.authorSun, Jinchang
dc.creator.authorHovig, Eivind
dc.creator.authorMyklebost, Ola
dc.creator.authorMeza-Zepeda, Leonardo A
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2164-15-449
dc.identifier.urnURN:NBN:no-51486
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/47378/1/12864_2013_Article_6212.pdf
dc.type.versionPublishedVersion
cristin.articleid449


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