PEMapper and PECaller provide a simplified approach to whole-genome sequencing

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dc.contributor.authorJohnston, Richard
dc.contributor.authorChopra, Pankaj
dc.contributor.authorWingo, Thomas
dc.contributor.authorPatel, Viren
dc.contributor.authorEpstein, Michael
dc.contributor.authorMulle, Jennifer
dc.contributor.authorWarren, Stephen
dc.contributor.authorZwick, Michael
dc.contributor.authorCutler, David
dc.contributor.authorInternational Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome
dc.date.accessioned2018-01-22T14:33:28Z
dc.date.available2018-01-22T14:33:28Z
dc.date.issued2017
dc.description.abstractThe analysis of human whole-genome sequencing data presents significant computational challenges. The sheer size of datasets places an enormous burden on computational, disk array, and network resources. Here, we present an integrated computational package, PEMapper/PECaller, that was designed specifically to minimize the burden on networks and disk arrays, create output files that are minimal in size, and run in a highly computationally efficient way, with the single goal of enabling whole-genome sequencing at scale. In addition to improved computational efficiency, we implement a statistical framework that allows for a base by base error model, allowing this package to perform as well or better than the widely used Genome Analysis Toolkit (GATK) in all key measures of performance on human whole-genome sequences.
dc.format.extent10
dc.identifier.citationProc Natl Acad Sci U S A. 2017 Mar 7;114(10):E1923-E1932
dc.identifier.urihttp://hdl.handle.net/11447/1929
dc.identifier.urihttp://dx.doi.org/10.1073/pnas.1618065114
dc.language.isoen_US
dc.publisherNational Academy of Sciences
dc.subjectGATK
dc.subjectSNP calling
dc.subjectgenome sequencing
dc.subjectsequence mapping
dc.subjectsoftware
dc.titlePEMapper and PECaller provide a simplified approach to whole-genome sequencing
dc.typeArtículo

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