Higher accuracy protein multiple sequence alignments by genetic algorithm | Zendy

Narayan Behera | Zendy; M.S. Jeevitesh | Zendy; Justin Jose | Zendy; Krishna Kant | Zendy; Alpana Dey | Zendy; Javed Mazher | Zendy

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Higher accuracy protein multiple sequence alignments by genetic algorithm

Author(s) -

Narayan Behera,

M.S. Jeevitesh,

Justin Jose,

Krishna Kant,

Alpana Dey,

Javed Mazher

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.05.100

Subject(s) - computer science , multiple sequence alignment , sequence (biology) , algorithm , sequence alignment , alignment free sequence analysis , structural alignment , protein sequencing , genetic algorithm , evolutionary algorithm , pattern recognition (psychology) , data mining , artificial intelligence , machine learning , peptide sequence , genetics , biology , gene

A Multiple sequence alignment (MSA) gives insight into the evolutionary, structural and functional relationships among the protein sequences. Here, the initial MSAs are chosen as the output of the two important protein sequence alignment programs: ProbCons and MCoffee. We have used the evolutionary operators of a genetic algorithm to find the optimized protein alignment after several iterations of the algorithm. Thus, we have developed a new MSA computational tool called as the Protein Alignment by Stochastic Algorithm (PASA). The efficiency of protein alignments is evaluated in terms of Total Column (TC) score. The TC score is basically the number of correctly aligned columns between the test alignments and the reference alignments divided by the total number of columns. The PASA is found to be statistically more accurate protein alignment method in our analysis in comparison to other popular bioinformatics tools.

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