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|a Weisenfeld, Neil I
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|a Massachusetts Institute of Technology. Department of Biology
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|a Lander, Eric S.
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|a Yin, Shuangye
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|a Sharpe, Ted
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|a Lau, Bayo
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|a Hegarty, Ryan
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|a Holmes, Laurie
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|a Sogoloff, Brian
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|a Tabbaa, Diana
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|a Williams, Louise
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|a Russ, Carsten
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|a Nusbaum, Chad
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|a MacCallum, Iain
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|a Jaffe, David B.
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|a Lander, Eric Steven
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|a Comprehensive variation discovery in single human genomes
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|b Nature Publishing Group,
|c 2015-06-05T15:14:06Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/97190
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|a Complete knowledge of the genetic variation in individual human genomes is a crucial foundation for understanding the etiology of disease. Genetic variation is typically characterized by sequencing individual genomes and comparing reads to a reference. Existing methods do an excellent job of detecting variants in approximately 90% of the human genome; however, calling variants in the remaining 10% of the genome (largely low-complexity sequence and segmental duplications) is challenging. To improve variant calling, we developed a new algorithm, DISCOVAR, and examined its performance on improved, low-cost sequence data. Using a newly created reference set of variants from the finished sequence of 103 randomly chosen fosmids, we find that some standard variant call sets miss up to 25% of variants. We show that the combination of new methods and improved data increases sensitivity by several fold, with the greatest impact in challenging regions of the human genome.
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|a National Human Genome Research Institute (U.S.) (Grant R01HG003474)
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|a National Human Genome Research Institute (U.S.) (Grant U54HG003067)
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|a National Institute of Allergy and Infectious Diseases (U.S.) (Contract HHSN272200900018C)
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|a en_US
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|a Article
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|t Nature Genetics
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