Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data.

Autor: Kennedy B; Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.; These authors collectively are the first authors of the unit., Kronenberg Z; Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.; These authors collectively are the first authors of the unit., Hu H; Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.; These authors collectively are the first authors of the unit., Moore B; Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah., Flygare S; Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah., Reese MG; Omicia, Inc, Emeryville, California., Jorde LB; Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah., Yandell M; Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah., Huff C; Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.
Jazyk: angličtina
Zdroj: Current protocols in human genetics [Curr Protoc Hum Genet] 2014 Apr 24; Vol. 81, pp. 6.14.1-6.14.25. Date of Electronic Publication: 2014 Apr 24.
DOI: 10.1002/0471142905.hg0614s81
Abstrakt: The VAAST pipeline is specifically designed to identify disease-associated alleles in next-generation sequencing data. In the protocols presented in this paper, we outline the best practices for variant prioritization using VAAST. Examples and test data are provided for case-control, small pedigree, and large pedigree analyses. These protocols will teach users the fundamentals of VAAST, VAAST 2.0, and pVAAST analyses.
(Copyright © 2014 John Wiley & Sons, Inc.)
Databáze: MEDLINE
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