TAILIEUCHUNG - Báo cáo sinh học: "Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics"

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: Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics. | Algorithms for Molecular Biology BioMed Central Open Access Effective p-value computations using Finite Markov Chain Imbedding FMCI application to local score and to pattern statistics Gregory Nuel Address Laboratoire Statistique et Génome UEVE CNRS 8071 INRA 1152 Evry France Email Grégory Nuel - nuel@ Corresponding author Published 07 April 2006 Received 15 February 2006 Algorithms for Molecular Biology 2006 1 5 doi 1748-7188-1-5 Accepted 07 April 2006 This article is available from http content 1 1 5 2006 Nuel 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 The technique of Finite Markov Chain Imbedding FMCI is a classical approach to complex combinatorial problems related to sequences. In order to get efficient algorithms it is known that such approaches need to be first rewritten using recursive relations. We propose here to give here a general recursive algorithms allowing to compute in a numerically stable manner exact Cumulative Distribution Function CDF or complementary CDF CCDF . These algorithms are then applied in two particular cases the local score of one sequence and pattern statistics. In both cases asymptotic developments are derived. For the local score our new approach allows for the very first time to compute exact p-values for a practical study finding hydrophobic segments in a protein database where only approximations were available before. In this study the asymptotic approximations appear to be completely unreliable for of the considered sequences. Concerning the pattern statistics the new FMCI algorithms dramatically outperform the previous ones as they are more reliable easier to implement faster and with lower memory requirements. 1 Introduction

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