PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A pairwise alignment algorithm which favours clusters of blocks
Elisabeth Gassiat, Elodie Nedelec, Thomas Moncion, Bruno Bossard, Guillemette Duchateau-Nguyen, Alain Denise and Michel Termier
Journal of Computational Biology Volume 12, Number 1, 2005.

This is the latest version of this eprint.


Pairwise sequence alignments aim to decide whether two sequences are related or not, and, if so, to exhibit their related domains. Recent works have pointed out that a significant amount of true homologous sequences are missed when using classical comparison algorithms. This is the case when two homologous sequences share several little bolcks of homology, too small to lead to a significant score. On the other hand,classical alignment algorithms, when detecting homologies, may fail to recognise all the significant biological signals. The aim of the paper is to give a solution to these two problems. We propose a new scoring method which tends to increase the score of an alignment when "blocks" are detected. This so-called "Bolck-Scoring" algorithm, which makes use of dynamic programming, is worth being used as complementary tool to classical exact alignment methods. We validate our approach by applying it on a large set of biological data. Finally, we give a limit theorem for the score statistics of the algorithm.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1718
Deposited By:Elisabeth Gassiat
Deposited On:28 November 2005

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