The optimization dialectical method for the multiple sequences alignment problem

The optimization dialectical method for the multiple sequences alignment problem

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Multiple sequence alignment (MSA) of biological sequences like DNA and proteins is one of the most important problems in bioinformatics, fundamental for the construction of phylogenetic trees, which are useful to establish evolutionary relationships among individuals and species. From the use of MSA methods, phylogenetic analysis could be conducted in order to reveal shared evolutionary origins. However, it is a very complex computational problem. Dialectical optimization is an evolutionary method designed to solve optimization and search problems using a social-evolutionary metaheuristic, based on the interaction of poles in a generation of solution candidates. Poles interact with each other along historical and crisis stages, in such a way that population sizes vary from one historical phase to another. Herein this work, we propose a dialectical approach to solve iteratively MSA problems, considering these problems as optimization tasks. We also propose an objective function based on some biological and computational constraints, in order to obtain feasible and biologically significant alignments. The results were compared with Clustal, a state-of-the-art MSA method, and proved to be reasonably useful, once alignment performances were comparable and, in some cases, our approach reaches superior scores. Our proposed method is also able to improve Clustal results using them as seeds for the dialectical optimization method.

Chapter Contents:

  • Abstract
  • 9.1 Introduction
  • 9.2 Materials and methods
  • 9.2.1 Optimization dialectical method
  • 9.2.2 Modeling
  • 9.2.3 Choice of the number of gaps to be used
  • 9.2.4 Objective function modeling
  • 9.2.5 Synthetic database
  • 9.2.6 Experiments
  • 9.2.7 Tuning ODM
  • 9.3 Results
  • 9.4 Discussion and conclusion
  • References

Inspec keywords: DNA; search problems; genetics; proteins; optimisation; evolutionary computation; trees (mathematics); evolution (biological); computational complexity; molecular biophysics

Other keywords: phylogenetic trees; biological constraints; alignment performances; biological sequences; historical phase; complex computational problem; search problems; computational constraints; MSA methods; historical crisis stages; species; shared evolutionary origins; evolutionary method; dialectical optimization method; MSA problems; multiple sequences alignment problem; evolutionary relationships; phylogenetic analysis

Subjects: Systems theory applications in biology and medicine; Biomolecular structure, configuration, conformation, and active sites; Combinatorial mathematics; Optimisation techniques; Macromolecular constitution (chains and sequences); Computational complexity; Algebra, set theory, and graph theory

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