We present practical parallel algorithms using prefix computations for various problems that arise in pairwise comparison of biological sequences. We consider both constant and affine gap penalty functions, full-sequence and subsequence matching, and space-saving algorithms. Commonly used sequential
Using Gaussian model to improve biological sequence comparison
β Scribed by Qi Dai; Xiaoqing Liu; Lihua Li; Yuhua Yao; Bin Han; Lei Zhu
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 398 KB
- Volume
- 31
- Category
- Article
- ISSN
- 0192-8651
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β¦ Synopsis
Abstract
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 kβwords in biological sequences directly, we considered the kβword frequency distribution under Gaussian model which gives the different expression levels of kβwords. 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 alignmentβbased and alignmentβfree methods. The results demonstrate that Gaussian model provides more information about kβword frequencies and improves the efficiency of sequence comparison. Β© 2009 Wiley Periodicals, Inc. J Comput Chem, 2010
π SIMILAR VOLUMES
The classical algorithms to align two biological sequences (Needleman and Wunsch and Smith and Waterman algorithms) can be seen as a sequence of elementary operations in (max; +) algebra: each line (viewed as a vector) of the dynamic programming table of the alignment algorithms can be deduced by a