A simulated annealing algorithm for finding consensus sequences

Keith, Jonathan M., Adams, Peter, Bryant, Darryn, Kroese, Dirk P., Mitchelson, Keith R., Cochran, Duncan A. E. and Lala, Gita H. (2002) A simulated annealing algorithm for finding consensus sequences. Bioinformatics, 18 11: 1494-1499. doi:10.1093/bioinformatics/18.11.1494

Author Keith, Jonathan M.
Adams, Peter
Bryant, Darryn
Kroese, Dirk P.
Mitchelson, Keith R.
Cochran, Duncan A. E.
Lala, Gita H.
Title A simulated annealing algorithm for finding consensus sequences
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
Publication date 2002
Sub-type Article (original research)
DOI 10.1093/bioinformatics/18.11.1494
Volume 18
Issue 11
Start page 1494
End page 1499
Total pages 6
Editor C. Sander
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2002
Language eng
Subject C1
230202 Stochastic Analysis and Modelling
780101 Mathematical sciences
Abstract Motivation: A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules. Results: This paper describes a new algorithm for finding a consensus sequence, using the popular optimization method known as simulated annealing. Unlike the conventional approach of finding a consensus sequence by first forming a multiple sequence alignment, this algorithm searches for a sequence that minimises the sum of pairwise distances to each of the input sequences. The resulting consensus sequence can then be used to induce a multiple sequence alignment. The time required by the algorithm scales linearly with the number of input sequences and quadratically with the length of the consensus sequence. We present results demonstrating the high quality of the consensus sequences and alignments produced by the new algorithm. For comparison, we also present similar results obtained using ClustalW. The new algorithm outperforms ClustalW in many cases.
Keyword Mathematics, Interdisciplinary Applications
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Statistics & Probability
Tree Alignment
Mathematical & Computational Biology
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Physical Sciences Publications
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Created: Tue, 14 Aug 2007, 17:06:19 EST