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Báo cáo y học: " Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays. | Open Access Method Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays Shin Lin Benilton Carvalho David J Cutler Dan E Arking Aravinda Chakravartn and Rafael A Irizarry Addresses McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine N. Broadway Baltimore MD 21205 USA. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health North Wolfe St. E3035 Baltimore MD 21205 USA. Correspondence Rafael A Irizarry. Email rafa@jhu.edu Published 3 April 2008 Genome Biology 2008 9 R63 doi 10.1 186 gb-2008-9-4-r63 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2008 9 4 R63 Received 26 August 2007 Revised 20 February 2008 Accepted 3 April 2008 2008 Lin et al. licensee 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 cited. Abstract Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms SNPs from Affymetrix microarrays. We extend and validate the algorithm CRLMM which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs BRLMM and Birdseed . Also we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods refered to as SNPaffycomp serve as standard benchmarks for future SNP calling algorithms. Background Genome-wide association studies hold great promise in discovering genes underlying complex heritable disorders for which less powerful study designs have failed in the past 1-3 . Much effort spanning academia and industry and across multiple disciplines has already been invested in making this type of study a reality with the .