A workflow for accelerated evolutionary analysis of genetic sequences

Authors

  • Felipe Santiago Carraro Eduardo Universidade do Estado do Rio de Janeiro
  • Igor dos Santos Rosa da Silva Universidade do Estado do Rio de Janeiro
  • Renan Pereira Souza Universidade do Estado do Rio de Janeiro
  • Alexandre da Costa Sena Universidade do Estado do Rio de Janeiro

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1295

Keywords:

Big Data, Genetic Distance, Workflow

Abstract

By nature, viruses are constantly mutating. Although most mutations do not change the behavior of a virus, some of these mutations can generate new variants, which, for example, can make a virus spread more quickly. One way to verify this evolution is through evolutive models. Therefore, the objective of this work is to evaluate the genetic evolution of viruses. The method used is pairwise alignment of the virus sequences, followed by calculation of genetic distance. Furthermore, to allow the evaluation of a large amount of sequence, these two steps are implemented through a Workflow. Results obtained through two case studies using the SARS-COV-2 and monkeypox viruses, showed not only the excellent performance of the workflow, considerably reducing the analysis execution time, but also the evolution of their genetic sequences.

Author Biographies

Felipe Santiago Carraro Eduardo, Universidade do Estado do Rio de Janeiro

Aluno de Mestrado, Instituto de Matemática e Estatística (IME), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro (RJ), Brasil.

Igor dos Santos Rosa da Silva, Universidade do Estado do Rio de Janeiro

Aluno de Mestrado, Instituto de Matemática e Estatística (IME), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro (RJ), Brasil.

Renan Pereira Souza, Universidade do Estado do Rio de Janeiro

Aluno de Mestrado, Instituto de Matemática e Estatística (IME), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro (RJ), Brasil.

Alexandre da Costa Sena, Universidade do Estado do Rio de Janeiro

Prof. Dr., Instituto de Matemática e Estatística (IME), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro (RJ), Brasil.

References

Farahat RA, Sah R, El-Sakka AA, others. Human monkeypox disease (MPX). Infez Med. 2022; 30: p. 372-391.

Hu B, Guo H, Zhou P, others. Characteristics of SARS-CoV-2 and COVID-19. Nature Reviews Microbiology. 2021; 19: p. 141-154.

Duffy S. Why are RNA virus mutation rates so damn high? PLOS Biology. 2018 August; 16: p. 1-6.

Verli H. Bioinformática: da Biologia à Flexibilidade Molecular. 1st ed.: Sociedade Brasileira de Bioquímica e Biologia Molecular - SBBq; 2014.

Junior MJ, Sena A, Rebello V. Fragmentando o DNA de Ferramentas de Alinhamento Progressivo: uma Metaferramenta Eficiente. Anais do XXIV Simp. em Sist. Comp. de Alto Desempenho; 2023; Porto Alegre, Brasil. p. 349–360.

Dezordi FZ, Neto AMD, Campos TL, Jeronimo PMC, Wallau GL. ViralFlow: A Versatile Automated Workflow for SARS-CoV-2 Genome Assembly, Lineage Assignment, Mutations and Intrahost Variant Detection. Viruses. 2022; 14: p. 217.

Kim K, Park K, Lee S, Baek SH, Lim TH, Kim J, et al. VirPipe: an easy-to-use and customizable pipeline for detecting viral genomes from Nanopore sequencing. Bioinformatics. 2023 May; 39: p. btad293.

Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nature Biotechnology. 2017; 35: p. 316–319.

De O. Sandes EF, Miranda G, Martorell X, Ayguade E, Teodoro G, De Melo ACMA. MASA: A Multiplatform Architecture for Sequence Aligners with Block Pruning. ACM Trans. Parallel Comput. 2016; 2 (4): p. 1-31.

Published

2024-11-19

How to Cite

Eduardo, F. S. C., da Silva, I. dos S. R., Souza, R. P., & Sena, A. da C. (2024). A workflow for accelerated evolutionary analysis of genetic sequences. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1295

Similar Articles

<< < 6 7 8 9 10 11 12 13 14 15 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)