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Báo cáo sinh học: "Computing distribution of scale independent motifs in biological sequences"

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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: Computing distribution of scale independent motifs in biological sequences. | Algorithms for Molecular Biology BioMed Central Open Access Computing distribution of scale independent motifs in biological sequences Jonas S Almeida 1 and Susana Vinga2 3 Address 1Dept Biostatistics and Applied Mathematics Univ. Texas MDAnderson Cancer Center 1515 Holcombe Blvd Houston TX 77030-4009 USA 2Instituto de Engenharia de Sistemas e Computadores Investigaẹão e Desenvolvimento INESC-ID R. Alves Redol 9 1000-029 Lisboa Portugal and 3Departamento de Bioestatística e Informatics Faculdade de Ciências Médicas - Universidade Nova de Lisboa FCM UNL Campo dos Mártires da Pátria 130 1169-056 Lisboa Portugal Email Jonas S Almeida - jalmeida@mdanderson.org Susana Vinga - svinga@algos.inesc-id.pt Corresponding author Published 18 October 2006 Received 03 May 2006 Algorithms for Molecular Biology 2006 1 18 doi 10.1186 1748-7188-1-18 Accepted 18 October 2006 This article is available from http www.almob.Org content 1 1 18 2006 Almeida and Vinga 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 use of Chaos Game Representation CGR or its generalization Universal Sequence Maps USM to describe the distribution of biological sequences has been found objectionable because of the fractal structure of that coordinate system. Consequently the investigation of distribution of symbolic motifs at multiple scales is hampered by an inexact association between distance and sequence dissimilarity. A solution to this problem could unleash the use of iterative maps as phasestate representation of sequences where its statistical properties can be conveniently investigated. In this study a family of kernel density functions is described that accommodates the .

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