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Computational intelligence in multiple sequence alignment

Computational intelligence in multiple sequence alignment Purpose – Multiple sequence alignment (MSA) is one of essential bioinformatics methods for decoding cis‐regulatory elements in gene regulation, predicting structure and function of proteins and RNAs, reconstructing phylogenetic tree, and other common tasks in biomolecular sequence analysis. The purpose of this paper is to describe briefly the basic concepts and formulations of gapped MSA and un‐gapped motif discovery approaches, and then review computational intelligence (CI) applications in MSA and motif‐finding problems. Design/methodology/approach – This paper performs exhaustive literature review on the MSA and motif discovery using CI techniques. Findings – Although CI‐based MSA algorithms were developed nearly a decade ago, most recent CI effort seems attempted to tackle the NP‐complete motif discovery problem. Applications of various CI techniques to solve motif discovery problem, including neural networks, self‐organizing map, genetic algorithms, swarm intelligence and combinations thereof, are surveyed. Finally, the paper concludes with discussion and perspective. Practical implications – The algorithms and software discussed in this paper can be used to align DNA, RNA and protein sequences, discover motifs, predict functions and structures of protein and RNA sequences, and estimate phylogenetic tree. Originality/value – The paper contributes to the first comprehensive survey of CI techniques that are applied to MSA and motif discovery. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Computational intelligence in multiple sequence alignment

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Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563780810857103
Publisher site
See Article on Publisher Site

Abstract

Purpose – Multiple sequence alignment (MSA) is one of essential bioinformatics methods for decoding cis‐regulatory elements in gene regulation, predicting structure and function of proteins and RNAs, reconstructing phylogenetic tree, and other common tasks in biomolecular sequence analysis. The purpose of this paper is to describe briefly the basic concepts and formulations of gapped MSA and un‐gapped motif discovery approaches, and then review computational intelligence (CI) applications in MSA and motif‐finding problems. Design/methodology/approach – This paper performs exhaustive literature review on the MSA and motif discovery using CI techniques. Findings – Although CI‐based MSA algorithms were developed nearly a decade ago, most recent CI effort seems attempted to tackle the NP‐complete motif discovery problem. Applications of various CI techniques to solve motif discovery problem, including neural networks, self‐organizing map, genetic algorithms, swarm intelligence and combinations thereof, are surveyed. Finally, the paper concludes with discussion and perspective. Practical implications – The algorithms and software discussed in this paper can be used to align DNA, RNA and protein sequences, discover motifs, predict functions and structures of protein and RNA sequences, and estimate phylogenetic tree. Originality/value – The paper contributes to the first comprehensive survey of CI techniques that are applied to MSA and motif discovery.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Mar 28, 2008

Keywords: Artificial intelligence; Neural nets; Biotechnology; Genetic testing; Programming and algorithm theory

References