Hybrid strategy in genetic algorithms to improve multiple sequence alignments

• Grant number: 19/00030-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: March 01, 2019
• End date: December 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Geraldo Francisco Donegá Zafalon
• Grantee: Vitoria Zanon Gomes
• Host Institution: Instituto de Biociências, Letras e Ciências Exatas (IBILCE). Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. São José do Rio Preto , SP, Brazil

Abstract

Multiple sequence alignment (MSA) is an important task in the context of bioinformatics, since it is used as a reference for various types of biological analyzes. There are different strategies based on several heuristics for the execution of AMS, such as progressive alignment (PA) and genetic algorithms (GA). The GA, unlike the PA, has the advantage of adapting its solutions throughout the execution of the algorithm, which generally allows to obtain good results of alignments, even with sets of sequences very similar. However, with genetic algorithms, local maximum points are easily reached, which negatively affects the quality of the alignments produced. Thus, the present work aims to model and implement a hybrid strategy of genetic algorithms for MSA, from the local refinement of regions of alignment with the strategy of progressive alignment. With this, it is expected that it will be possible to alleviate the problem of maximum locality of GAs, in order to offer an approach that is capable of producing results with greater biological significance.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GOMES, VITORIA ZANON; ANDRADE, MATHEUS CARREIRA; AMORIM, ANDERSON RICI; DONEGA ZAFALON, GERALDO FRANCISCO; FILIPE, J; SMIALEK, M; BRODSKY, A; HAMMOUDI, S. A Hybrid Genetic Algorithm using Progressive Alignment and Consistency based Approach for Multiple Sequence Alignments. ICEIS: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, v. N/A, p. 8-pg., 2022-01-01. (19/00030-3)

DONEGA ZAFALON, GERALDO FRANCISCO; GOMES, VITORIA ZANON; AMORIM, ANDERSON RICI; VALENCIO, CARLOS ROBERTO; FILIPE, J; SMIALEK, M; BRODSKY, A; HAMMOUDI, S. A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments. PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS 2021), VOL 1, v. N/A, p. 8-pg., 2021-01-01. (19/00030-3)