tailieunhanh - Báo cáo y học: "Novel gene and gene model detection using a whole genome open reading frame analysis in proteomics"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Novel gene and gene model detection using a whole genome open reading frame analysis in proteomics. | Research Open Access Novel gene and gene model detection using a whole genome open reading frame analysis in proteomics Damian Fermin Baxter B Allen Thomas W Blackwell Rajasree Menon Marcin Adamski Yin Xu Peter Ulintz Gilbert S Omenn and David J States Addresses Bioinformatics Program University of Michigan Ann Arbor MI 48109 USA. Department of Internal Medicine University of Michigan Ann Arbor MI 48109 USA. Department of Human Genetics University of Michigan Ann Arbor MI 48109 USA. Correspondence DavidJ States. Email dstates@ Published 28 April 2006 Genome Biology 2006 7 R35 doi 186 gb-2006-7-4-r35 The electronic version of this article is the complete one and can be found online at http 2006 7 4 R35 Received 5 January 2006 Revised 22 February 2006 Accepted 27 March 2006 2006 Fermin et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Defining the location of genes and the precise nature of gene products remains a fundamental challenge in genome annotation. Interrogating tandem mass spectrometry data using genomic sequence provides an unbiased method to identify novel translation products. A six-frame translation of the entire human genome was used as the query database to search for novel blood proteins in the data from the Human Proteome Organization Plasma Proteome Project. Because this target database is orders of magnitude larger than the databases traditionally employed in tandem mass spectra analysis careful attention to significance testing is required. Confidence of identification is assessed using our previously described Poisson statistic which estimates the significance of multi-peptide identifications incorporating the length of the matching sequence number