Optimization of multiple sequence alignment methodologies using a multiobjective evolutionary algorithm based on NSGA-II

Multiple sequence alignment (MSA) is one of the most studied approach in Bioinformatics to carry out other outstanding tasks like structural predictions, biological function analysis or next-generation sequencing. However, MSA algorithms do not achieve consistent results in all cases, as alignments...

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Bibliographic Details
Main Authors: Ortuno, F., Florido, J. P., Urquiza, J. M., Pomares, H., Prieto, A., Rojas, I.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Biological cells
Clustering algorithms
evolutionary algorithms
Evolutionary computation
multiobjective
multiple sequence alignment (MSA)
Muscles
NSGA-II
Optimization
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Description
Summary:Multiple sequence alignment (MSA) is one of the most studied approach in Bioinformatics to carry out other outstanding tasks like structural predictions, biological function analysis or next-generation sequencing. However, MSA algorithms do not achieve consistent results in all cases, as alignments become difficult when sequences have low similarity. In other words, each algorithm is focused in specific features of sequences and their results depend on them. For this reason, each approach could align better those sections of sequences that include such features, obtaining partially optimal solutions. In this work, a multiobjective evolutionary algorithm based on NSGA-II will be implemented in order to assemble previously aligned sequences, trying to avoid suboptimal alignments.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2012.6256146