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Using Gaussian model to improve biological sequence comparison
Author(s) -
Dai Qi,
Liu Xiaoqing,
Li Lihua,
Yao Yuhua,
Han Bin,
Zhu Lei
Publication year - 2009
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.21322
Subject(s) - gaussian , sequence (biology) , similarity (geometry) , phylogenetic tree , computer science , biological data , word (group theory) , algorithm , multiple sequence alignment , gaussian network model , pattern recognition (psychology) , artificial intelligence , computational biology , sequence alignment , mathematics , biology , bioinformatics , gene , genetics , physics , peptide sequence , geometry , quantum mechanics , image (mathematics)
One of the major tasks in biological sequence analysis is to compare biological sequences, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Numerous efficient methods have been developed for sequence comparison, but challenges remain. In this article, we proposed a novel method to compare biological sequences based on Gaussian model. Instead of comparing the frequencies of k ‐words in biological sequences directly, we considered the k ‐word frequency distribution under Gaussian model which gives the different expression levels of k ‐words. The proposed method was tested by similarity search, evaluation on functionally related genes, and phylogenetic analysis. The performance of our method was further compared with alignment‐based and alignment‐free methods. The results demonstrate that Gaussian model provides more information about k ‐word frequencies and improves the efficiency of sequence comparison. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010