Improvements in the sensibility of MSA-GA tool using COFFEE objective function

3rd International Conference on Mathematical Modeling in Physical Sciences

物理学;数学

Amorim, A.R.^1 ; Zafalon, G.F.D.^1 ; Neves, L.A.^1 ; Pinto, A.R.^2 ; Valêncio, C.R.^1 ; Machado, J.M.^1

Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto, Brazil^1

Department of Control Engineering and Automation, Federal University of Santa Catarina, Blumenau, Brazil^2

关键词: Ant colonies;    Deterministic algorithms;    Objective functions;    Sequence alignments;    Sequences analysis;    Weighted Sum;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/574/1/012104/pdf
DOI  :  10.1088/1742-6596/574/1/012104

来源: IOP

【 摘 要 】

The sequence alignment is one of the most important tasks in Bioinformatics, playing an important role in the sequences analysis. There are many strategies to perform sequence alignment, since those use deterministic algorithms, as dynamic programming, until those ones, which use heuristic algorithms, as Progressive, Ant Colony (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), among others. In this work, we have implemented the objective function COFFEE in the MSA-GA tool, in substitution of Weighted Sum-of-Pairs (WSP), to improve the final results. In the tests, we were able to verify the approach using COFFEE function achieved better results in 81% of the lower similarity alignments when compared with WSP approach. Moreover, even in the tests with more similar sets, the approach using COFFEE was better in 43% of the times.