Article

# Sequence alignment with an appropriate substitution matrix.

Department of Computer Science, Iowa State University, Ames, Iowa 50011-1040, USA.

Journal of Computational Biology (Impact Factor: 1.67). 04/2008; 15(2):129-38. DOI: 10.1089/cmb.2007.0155 Source: PubMed

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**ABSTRACT:**Pairwise sequence alignment forms the basis of numerous other applications in bioinformatics. The quality of an alignment is gauged by statistical significance rather than by alignment score alone. Therefore, accurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, it was shown that pairwise statistical significance does better in practice than database statistical significance, and also provides quicker individual pairwise estimates of statistical significance without having to perform time-consuming database search. Under an evolutionary model, a substitution matrix can be derived using a rate matrix and a fixed distance. Although the commonly used substitution matrices like BLOSUM62, etc. were not originally derived from a rate matrix under an evolutionary model, the corresponding rate matrices can be back calculated. Many researchers have derived different rate matrices using different methods and data. In this paper, we show that pairwise statistical significance using rate matrices with sequence-pair-specific distance performs significantly better compared to using a fixed distance. Pairwise statistical significance using sequence-pair-specific distanced substitution matrices also outperforms database statistical significance reported by BLAST.Information Technology, International Conference on. 12/2008; - [Show abstract] [Hide abstract]

**ABSTRACT:**We present a new formulation of phylogenetic reconstruction named maximum similarity. We describe basic algorithms based on the maximum similarity objective for computing distances between subtrees and for combining two subtrees. We present distance methods for constructing an initial tree and updating the initial tree by incorporating those basic algorithms into the Neighbor Joining (NJ) method and the Nearest-Neighbor Interchange (NNI) framework of the FastME program. The new distance methods have been implemented as a computer program named MS. The time requirement of the MS program is about five times the cost of computing observed sequence distances. The MS program was compared by simulation with four existing programs: NJ, FastME, STC, and Weighbor. Experimental results show that incorporating the maximum similarity objective into existing methods leads to improvements both in topology and in branch length.Journal of computational biology: a journal of computational molecular cell biology 08/2009; 16(7):887-96. · 1.69 Impact Factor

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