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Báo cáo sinh học: "Refining motifs by improving information content scores using neighborhood profile searc"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Refining motifs by improving information content scores using neighborhood profile search. | Algorithms for Molecular Biology BioMed Central Research Refining motifs by improving information content scores using neighborhood profile search Chandan K Reddy Yao-Chung Weng and Hsiao-Dong Chiang Open Access Address School of Electrical and Computer Engineering Cornell University Ithaca NY 14853 USA Email Chandan K Reddy - ckr6@cornell.edu Yao-Chung Weng - ycwweng@gmail.com Hsiao-Dong Chiang - chiang@ece.cornell.edu Corresponding author Published 27 November 2006 Received 20 July 2006 J . Accepted 27 November 2006 Algorithms for Molecular Biology 2006 1 23 doi 10.1186 1748-7188-1-23 This article is available from http www.almob.org content 1 1 23 2006 Reddy 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 The main goal of the motif finding problem is to detect novel over-represented unknown signals in a set of sequences e.g. transcription factor binding sites in a genome . The most widely used algorithms for finding motifs obtain a generative probabilistic representation of these overrepresented signals and try to discover profiles that maximize the information content score. Although these profiles form a very powerful representation of the signals the major difficulty arises from the fact that the best motif corresponds to the global maximum of a non-convex continuous function. Popular algorithms like Expectation Maximization EM and Gibbs sampling tend to be very sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. In order to improve the quality of the results EM is used with multiple random starts or any other powerful stochastic global methods that might yield promising initial guesses like projection algorithms . Global methods do not necessarily give