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Online Journal of Bioinformatics ©
Volume 12(1):74-84, 2011
Self organizing genetic algorithms for local and global multiple sequence alignment
Amouda V1*, Buvaneswari S1, Kuppuswami S2
1Centre for Bioinformatics, 2Department of Computer Science R.V. Nagar, Kalapet, Pondicherry University, Puducherry- 605 014
Amouda V, Buvaneswari S, Kuppuswami S, Self organizing genetic algorithms for local and global multiple sequence alignment, Online J Bioinformatics, 12(1): 74-84, 2011. A global multiple sequence alignment algorithm (SOGA) includes 2 new operators which perform a self organizing crossover and mutation operation for the required number of generations. Another algorithm (SOSW) performs pairwise alignment and progressively aligns it to a local multiple sequence alignment (MSA) based on the alignment score. Many MSA tools and genetic alignment algorithms have parameter values either as default or optional making it difficult to select appropriate method and parameter values since the sequence is unknown. The proposed algorithms select appropriate alignment and parameter values based on sequence length, identity and alignment score, reducing time to optimize parameter values and prevent execution with default values. A score columm then compares alignment to validate results.