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: KEGG spider: interpretation of genomics data in the context of the global gene metabolic network. | Open Access Method KEGG spider interpretation of genomics data in the context of the global gene metabolic network Alexey V Antonov Sabine Dietmann and Hans W Mewes Addresses GSF National Research Centre for Environment and Health Institute for Bioinformatics Ingolstadter LandstraBe 1 D-85764 Neuherberg Germany. Department of Genome-Oriented Bioinformatics Wissenschaftszentrum Weihenstephan Technische Universitat Munchen 85350 Freising Germany. Correspondence Alexey V Antonov. Email antonov@ Published 18 December 2008 Genome Biology 2008 9 R179 doi gb-2008-9- 12-r 179 The electronic version of this article is the complete one and can be found online at http 2008 9 12 R179 2009 Antonov 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. Received 7 August 2008 Revised 28 October 2008 Accepted l8 December 2008 Abstract KEGG spider is a web-based tool for interpretation of experimentally derived gene lists in order to gain understanding of metabolism variations at a genomic level. KEGG spider implements a pathway-free framework that overcomes a major bottleneck of enrichment analyses it provides global models uniting genes from different metabolic pathways. Analyzing a number of experimentally derived gene lists we demonstrate that KEGG spider provides deeper insights into metabolism variations in comparison to existing methods. Background In the post-genomic era the targets of many experimental studies are complex cell disorders 1-6 . A standard experimental strategy is to compare the genetic proteomics signatures of cells in normal and anomalous states. As a result a set of genes with differential activity is delivered. In the next step the interpretation of identified genes in a model .
