Self-Organizing Genetic Algorithm for Multiple Sequence Alignment

Genetic algorithm (GA) used to solve the optimization problem is self-organized and applied to Multiple Sequence Alignment (MSA), an essential process in molecular sequence analysis. This paper presents the first attempt in applying Self-Organizing Genetic Algorithm for MSA. Self-organizing genetic algorithm (SOGA) can be developed with the complete knowledge about the problem and its parameters. In SOGA, values of various parameters are decided based on the problem and fitness value obtained in each generation. The proposed algorithm undergoes a self-organizing crossover operation by selecting an appropriate rate or a point and a self-organizing cyclic mutation for the required number of generations. The advantages of the proposed algorithm are (i) reduce the time requirement for optimizing the parameter values (ii) prevent execution with default values (iii) avoid premature convergence by the cyclic mutation operation. To validate the efficiency, SOGA is applied to MSA, and the resulting alignment is evaluated using the column score (CS). The comparison result shows that the alignment produced by SOGA is better than the widely used tools like Dialign and Multalin. It is also evident that the proposed algorithm can produce optimal or closer-to-optimal alignment compared to tools like ClustalW, Mafft, Dialign and Multalin

Alignment of Multiple DNA Sequences by Using Improved GA Operators

ABSTRACT One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). It is a critical tool for biologists to identify the relationships between species and also possibly predict the structure and functionality of biological sequences. The general multiple sequence alignment problem is known to be NP-hard, and hence the problem of finding the best possible multiple sequence alignment is intractable. Therefore, a genetic algorithm based approach has been designed to solve the multiple DNA sequence alignment problem by using different genetic operators. Experimental results with different lengths of DNA sequences has been detailed in this paper . It has also shown that how the increase in length will affect the overall quality of the alignment. The extensive experiment on wide range of datasets and the obtained results has shown the effectiveness of the proposed approach in solving multiple DNA sequences. KEYWORDS: Multiple Sequence Alignment, Genetic Algorithms (GAs), DNA Sequences. INTRODUCTION The main components of the biochemical processes of life are proteins and nucleic acids. There are two types of nucleic acids, deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA sequences are long biomolecular strands composed of four types of nucleotide bases: adenine (A), guanine (G), cytosine (C), and thymine (T). DNA actually occurs as a double strand of such bases. The stands are held together by hydrogen bonds between complementary bases: A-T and G-C. DNA sequences, which consist of hundreds of millions of nucleotides, define the genome of a particular species. Recent advances in bioinformatics have generated volumes of genome data for biomedical research. For example, many immunity genes in the fruit fly genome have nucleotide sequences that are reminiscent of TCGGGGATTTC

Aligning Multiple Sequences with Genetic Algorithm

The alignment of biological sequences is a crucial tool in molecular biology and genome analysis. It helps to build a phylogenetic tree of related DNA sequences and also to predict the function and structure of unknown protein sequences by aligning with other sequences whose function and structure is already known. However, finding an optimal multiple sequence alignment takes time and space exponential with the length or number of sequences increases. Genetic Algorithms (GAs) are strategies of random searching that optimize an objective function which is a measure of alignment quality (distance) and has the ability for exploratory search through the solution space and exploitation of current results

Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis

Understanding protein-protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein-protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue-residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has in turn been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being co-localized in operons. Here we show that the Direct-Coupling Analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify inter-protein residue-residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.Comment: Main Text 19 pages Supp. Inf. 16 page