Abstract
BackgroundMultiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics. MSA is mainly focused on discovering biologically meaningful relationships among different sequences or proteins in order to investigate the underlying main characteristics/functions. This information is also used to generate phylogenetic trees.ResultsThis paper presents a novel approach, namely RBT-GA, to solve the MSA problem using a hybrid solution methodology combining the Rubber Band Technique (RBT) and the Genetic Algorithm (GA) metaheuristic. RBT is inspired by the behavior of an elastic Rubber Band (RB) on a plate with several poles, which is analogues to locations in the input sequences that could potentially be biologically related. A GA attempts to mimic the evolutionary processes of life in order to locate optimal solutions in an often very complex landscape. RBT-GA is a population based optimization algorithm designed to find the optimal alignment for a set of input protein sequences. In this novel technique, each alignment answer is modeled as a chromosome consisting of several poles in the RBT framework. These poles resemble locations in the input sequences that are most likely to be correlated and/or biologically related. A GA-based optimization process improves these chromosomes gradually yielding a set of mostly optimal answers for the MSA problem.ConclusionRBT-GA is tested with one of the well-known benchmarks suites (BALiBASE 2.0) in this area. The obtained results show that the superiority of the proposed technique even in the case of formidable sequences.
Highlights
Multiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics
Rubber Band Technique (RBT)-Genetic Algorithm (GA) and Reference #3 Reference #3 of the BALiBASE 2.0 is dedicated to subgroups of sequences with less than 25% residue identity between groups
RBT-GA is a population based optimization algorithm that starts from a set of possible answers, and gradually improves it to find the optimal alignment
Summary
Multiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics. MSA is mainly focused on discovering biologically meaningful relationships among different sequences or proteins in order to investigate the underlying main characteristics/ functions. This information is used to generate phylogenetic trees. Sequence Alignment algorithms are techniques that are used to find similarity among several DNA/Protein sequences. These algorithms are classified into two main categories: Pair-wise and MSA algorithms, each designed for a special purpose. Pair-wise algorithms are mainly used to find similar sequences in a database; MSAs are mainly used to find the relationship among several sequences
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