Analysis of Bioinformatic algorithms for MSA

Aligning three or more biological sequences, such as DNA, RNA, or protein, is known as multiple sequence alignment (MSA). MSA is crucial in identifying important information about the sequences, including function, evolution, and structure. It serves as the first step in analyzing phylogenetic, prot...

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Detalles Bibliográficos
Autores principales: Díaz, Adrián, Minetti, Gabriela F.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2023
Materias:
Ciencias Informáticas
Multiple sequence alignment
Bioinformatics
Simulated Annealing
Metaheuristics
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/164877
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-164877
record_format dspace
spelling I19-R120-10915-1648772024-04-16T20:03:03Z http://sedici.unlp.edu.ar/handle/10915/164877 Analysis of Bioinformatic algorithms for MSA Díaz, Adrián Minetti, Gabriela F. 2023-10 2024 2024-04-16T14:28:42Z en Ciencias Informáticas Multiple sequence alignment Bioinformatics Simulated Annealing Metaheuristics Aligning three or more biological sequences, such as DNA, RNA, or protein, is known as multiple sequence alignment (MSA). MSA is crucial in identifying important information about the sequences, including function, evolution, and structure. It serves as the first step in analyzing phylogenetic, protein, and genomic data. However, as sequence scale increases and the demand for alignment accuracy grows, MSA faces new challenges. Therefore, developing an efficient and precise tool for MSA and comparing its performance with existing ones has become a research hotspot in Bioinformatics. In this magister thesis, we propose a metaheuristic algorithm to solve MSA and a methodology to compare the performance of algorithms for aligning multiple sequences. Red de Universidades con Carreras en Informática Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 81-86
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Multiple sequence alignment
Bioinformatics
Simulated Annealing
Metaheuristics
spellingShingle Ciencias Informáticas
Multiple sequence alignment
Bioinformatics
Simulated Annealing
Metaheuristics
Díaz, Adrián
Minetti, Gabriela F.
Analysis of Bioinformatic algorithms for MSA
topic_facet Ciencias Informáticas
Multiple sequence alignment
Bioinformatics
Simulated Annealing
Metaheuristics
description Aligning three or more biological sequences, such as DNA, RNA, or protein, is known as multiple sequence alignment (MSA). MSA is crucial in identifying important information about the sequences, including function, evolution, and structure. It serves as the first step in analyzing phylogenetic, protein, and genomic data. However, as sequence scale increases and the demand for alignment accuracy grows, MSA faces new challenges. Therefore, developing an efficient and precise tool for MSA and comparing its performance with existing ones has become a research hotspot in Bioinformatics. In this magister thesis, we propose a metaheuristic algorithm to solve MSA and a methodology to compare the performance of algorithms for aligning multiple sequences.
format Objeto de conferencia
Objeto de conferencia
author Díaz, Adrián
Minetti, Gabriela F.
author_facet Díaz, Adrián
Minetti, Gabriela F.
author_sort Díaz, Adrián
title Analysis of Bioinformatic algorithms for MSA
title_short Analysis of Bioinformatic algorithms for MSA
title_full Analysis of Bioinformatic algorithms for MSA
title_fullStr Analysis of Bioinformatic algorithms for MSA
title_full_unstemmed Analysis of Bioinformatic algorithms for MSA
title_sort analysis of bioinformatic algorithms for msa
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/164877
work_keys_str_mv AT diazadrian analysisofbioinformaticalgorithmsformsa
AT minettigabrielaf analysisofbioinformaticalgorithmsformsa
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