Author: Dai, Qi; Liu, Xiaoqing; Li, Lihua; Yao, Yuhua; Han, Bin; Zhu, Lei
Title: Using Gaussian model to improve biological sequence comparison Cord-id: yjon4plr Document date: 2009_5_28
ID: yjon4plr
Snippet: 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 kâ€words in biological sequences directly
Document: 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 kâ€words in biological sequences directly, we considered the kâ€word frequency distribution under Gaussian model which gives the different expression levels of kâ€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 alignmentâ€based and alignmentâ€free methods. The results demonstrate that Gaussian model provides more information about kâ€word frequencies and improves the efficiency of sequence comparison. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010
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