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.
This paper performs exhaustive literature review on the MSA and motif discovery using CI techniques.
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.
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.
The paper contributes to the first comprehensive survey of CI techniques that are applied to MSA and motif discovery.
Bi, C. (2008), "Computational intelligence in multiple sequence alignment", International Journal of Intelligent Computing and Cybernetics, Vol. 1 No. 1, pp. 8-24. https://doi.org/10.1108/17563780810857103Download as .RIS
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