Mathematical Understanding of Sequence Alignment and Phylogenetic Algorithms: A Comprehensive Review of Methods

Context: Pairwise sequence alignment is one of the ways to arrange two biological sequences to identify regions of resemblance that may suggest the functional, structural, and/or evolutionary relationship (proteins or nucleic acids) between the sequences. There are two strategies in pairwise sequence alignment: Local sequence Alignment (Smith-waterman algorithm) and Global sequence Alignment (Needleman-Wunsch algorithm). In local sequence alignment, two sequences that may or may not be related are aligned to find regions of local similarities in large sequences whereas in global sequence alignment, two sequences same in length are aligned to identify conserved regions. Similarities and divergence between biological sequences identified by sequence alignment also have to be rationalized and visualized in the sense of phylogenetic trees. The phylogenetic tree construction methods are divided into distance-based and character-based methods. Evidence Acquisition: In this article, different algorithms of sequence alignment and phylogenetic tree construction were studied with examples and compared to establish the best among them to look into background of these methods for the better understanding of computational phylogenetics.Conclusions: Pairwise sequence alignment is a very important part of bioinformatics to compare biological sequences to find similarities among them. The alignment data is visualized through phylogenetic tree diagram that shows evolutionary history among organisms. Phylogenetic tree is constructed through various methods some are easier but does not provide accurate evolutionary data whereas others provide accurate evolutionary distance among organism but are computationally exhaustive.

An Improved Hybridized Evolutionary Algorithm Based on Rules for Local Sequence Alignment

In bioinformatics, sequence alignment is the heart of the sequence analysis. Sequence can be aligned locally or globally depending upon the biologist's need for the analysis. As local sequence alignment is considered important, there is demand for an efficient algorithm. Due to the enormous sequences in the biological database, there is a trade-off between computational time and accuracy. In general, all biological problems are considered as computational intensive problems. To solve these kinds of problems, evolutionary-based algorithms are proficiently used. This chapter focuses local alignment in molecular sequences and proposes an improvised hybrid evolutionary algorithm using particle swarm optimization and cellular automata (IPSOCA). The efficiency of the proposed algorithm is proved using the experimental analysis for benchmark dataset BaliBase and compared with other state-of-the-art techniques. Using the Wilcoxon matched pair signed rank test, the significance of the proposed algorithm is explicated.