Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do LNCC |
Texto Completo: | https://tede.lncc.br/handle/tede/351 |
Resumo: | In the last years, the development of technologies, such as next-generation sequencing and high-performance computing allowed the execution of Bioinformatics experiments of high complexity and computationally intensives. Different Bioinformatics fields need to use high-performance computing platforms to take advantage of the parallelism and tasks distribution, through specialized technologies of scientific workflows management systems. One of the Bioinformatics fields that need high-performance computing is phylogeny, a field that expresses the evolutive relations between genes and organisms, establishing which of them are most related evolutively. The phylogeny is used in several approaches, such as in the species classification; in the discovery of individuals’ kinship; in the identification of pathogens origins, and even in conservation biology. A way of representing these phylogenetic relations is using phylogenetic networks. However, the construction of these networks uses computationally intensive algorithms that require the constant manipulation of different input data. This work aims the development of a framework for construction of explicit phylogenetic networks, modeling a scientific workflow that adds different methods for the construction of the networks and the required input data treatment. The framework was developed to allow the use of multiple flows from the workflow in an automated, parallel, and distributed manner in a single execution and also to be executable in high- performance computing environments, constituting a challenging task, once the tools used are not developed focused in this environment. To orchestrate the workflow tasks, the scalable parallel programing library Parsl was used, allowing to do optimizations in the workflow’s tasks execution, performing better management of the resources. Two versions of the framework were developed, called Single Partition and Multi Partition, differing in the manner in which the resources are used. In tests performed, there was an improvement in the execution time of about five times when compared to the sequential execution of a flow without the optimizations. The framework was validated using public data of Dengue virus genomes, which were processed, annotated, and executed in the framework using the Santos Dumont supercomputer. The construction of the genomes’ explicit phylogenetic networks indicates that the framework is a functional, efficient, and easy to use tool. |
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Gautherot, Kary Ann del Carmen OcañaBarros, Carla Osthoff Ferreira deCarvalho, Diego moreira de AraújoGautherot, Kary Ann del Carmen OcañaSantos, Marcelo Trindade dosGadelha Júnior, Luiz Manoel RochaCastro, Maria Clicia Stelling deSilva, Fabrício Alves Barbosa dahttp://lattes.cnpq.br/8426017745859480Terra, Rafael de Souza2023-04-18T14:57:05Z2022-02-18TERRA, R. S. Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho. 2022. 71 f. Tese. (Programa de Pós-Graduação em Modelagem Computacional) - Laboratório Nacional de Computação Científica, Petrópolis, 2022.https://tede.lncc.br/handle/tede/351In the last years, the development of technologies, such as next-generation sequencing and high-performance computing allowed the execution of Bioinformatics experiments of high complexity and computationally intensives. Different Bioinformatics fields need to use high-performance computing platforms to take advantage of the parallelism and tasks distribution, through specialized technologies of scientific workflows management systems. One of the Bioinformatics fields that need high-performance computing is phylogeny, a field that expresses the evolutive relations between genes and organisms, establishing which of them are most related evolutively. The phylogeny is used in several approaches, such as in the species classification; in the discovery of individuals’ kinship; in the identification of pathogens origins, and even in conservation biology. A way of representing these phylogenetic relations is using phylogenetic networks. However, the construction of these networks uses computationally intensive algorithms that require the constant manipulation of different input data. This work aims the development of a framework for construction of explicit phylogenetic networks, modeling a scientific workflow that adds different methods for the construction of the networks and the required input data treatment. The framework was developed to allow the use of multiple flows from the workflow in an automated, parallel, and distributed manner in a single execution and also to be executable in high- performance computing environments, constituting a challenging task, once the tools used are not developed focused in this environment. To orchestrate the workflow tasks, the scalable parallel programing library Parsl was used, allowing to do optimizations in the workflow’s tasks execution, performing better management of the resources. Two versions of the framework were developed, called Single Partition and Multi Partition, differing in the manner in which the resources are used. In tests performed, there was an improvement in the execution time of about five times when compared to the sequential execution of a flow without the optimizations. The framework was validated using public data of Dengue virus genomes, which were processed, annotated, and executed in the framework using the Santos Dumont supercomputer. The construction of the genomes’ explicit phylogenetic networks indicates that the framework is a functional, efficient, and easy to use tool.Nos últimos anos, o desenvolvimento de tecnologias, como o sequenciamento de nova geração e a computação de alto desempenho possibilitou a execução de experimentos de Bioinformática de alta complexidade e computacionalmente intensivos. Diferentes áreas da Bioinformática necessitam utilizar plataformas de computação de alto desempenho para aproveitar do paralelismo e da distribuição de tarefas, por meio de tecnologias especializadas de sistemas de gerência de workflows científicos. Uma das áreas da Bioinformática que necessitam da computação de alto desempenho é a filogenia, área que expressa as relações evolutivas entre genes e organismos, estabelecendo quais deles estão mais relacionados evolutivamente. A filogenia é usada em várias abordagens, como na classificação de espécies; na descoberta do parentesco de indivíduos; na identificação da origem de patógenos, e até na biologia da conservação. Uma forma de representar as relações filogenéticas é utilizando redes filogenéticas. Contudo, a construção dessas redes utiliza algoritmos computacionalmente intensivos e que requerem a constante manipulação dos diferentes dados de entrada. O presente trabalho visa o desenvolvimento de um framework para a construção de redes filogenéticas explícitas, modelando um workflow científico que agrega diferentes métodos para a construção das redes e para o tratamento dos dados de entrada necessários. O framework foi desenvolvido para possibilitar a utilização de múltiplos fluxos do workflow de forma automatizada, paralela e distribuída em uma única execução e também ser executável em ambientes de computação de alto desempenho, configurando uma tarefa desafiadora, uma vez que as ferramentas usadas não são desenvolvidas com foco nesse ambiente. Para orquestrar as tarefas do workflow, utilizou-se a biblioteca de programação paralela escalável Parsl, permitindo realizar otimizações na execução das tarefas do workflow, realizando um melhor controle de recursos. Foram desenvolvidas duas versões do framework, chamadas Single Partition e Multi Partition, diferindo na forma como os recursos são utilizados. Nos testes realizados, houve uma melhoria no tempo de execução de aproximadamente cinco vezes em comparação com a execução sequencial de um fluxo sem as otimizações. O framework foi validado utilizando dados públicos de genomas do vírus da Dengue, que foram processados, anotados e executados no framework utilizando o supercomputador Santos Dumont. A construção das redes filogenéticas explícitas dos genomas indicam que o framework desenvolvido é uma ferramenta funcional, eficiente e de fácil uso.Submitted by Patrícia Vieira Silva (library@lncc.br) on 2023-04-18T14:55:58Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) dissertacao_Rafael de Souza Terra.pdf: 1970431 bytes, checksum: d8856ca0e2bbf4b2ebb581c03d5f9358 (MD5)Approved for entry into archive by Patrícia Vieira Silva (library@lncc.br) on 2023-04-18T14:56:25Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) dissertacao_Rafael de Souza Terra.pdf: 1970431 bytes, checksum: d8856ca0e2bbf4b2ebb581c03d5f9358 (MD5)Made available in DSpace on 2023-04-18T14:57:05Z (GMT). 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dc.title.por.fl_str_mv |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
title |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
spellingShingle |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho Terra, Rafael de Souza Bioinformática Computação de alto desempenho Biotecnologia Fluxo de trabalho Framework (Programa de computador) Dengue CNPQ::CIENCIAS BIOLOGICAS::BIOLOGIA GERAL |
title_short |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
title_full |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
title_fullStr |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
title_full_unstemmed |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
title_sort |
Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho |
author |
Terra, Rafael de Souza |
author_facet |
Terra, Rafael de Souza |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Gautherot, Kary Ann del Carmen Ocaña |
dc.contributor.advisor2.fl_str_mv |
Barros, Carla Osthoff Ferreira de |
dc.contributor.advisor-co1.fl_str_mv |
Carvalho, Diego moreira de Araújo |
dc.contributor.referee1.fl_str_mv |
Gautherot, Kary Ann del Carmen Ocaña |
dc.contributor.referee2.fl_str_mv |
Santos, Marcelo Trindade dos |
dc.contributor.referee3.fl_str_mv |
Gadelha Júnior, Luiz Manoel Rocha |
dc.contributor.referee4.fl_str_mv |
Castro, Maria Clicia Stelling de |
dc.contributor.referee5.fl_str_mv |
Silva, Fabrício Alves Barbosa da |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8426017745859480 |
dc.contributor.author.fl_str_mv |
Terra, Rafael de Souza |
contributor_str_mv |
Gautherot, Kary Ann del Carmen Ocaña Barros, Carla Osthoff Ferreira de Carvalho, Diego moreira de Araújo Gautherot, Kary Ann del Carmen Ocaña Santos, Marcelo Trindade dos Gadelha Júnior, Luiz Manoel Rocha Castro, Maria Clicia Stelling de Silva, Fabrício Alves Barbosa da |
dc.subject.por.fl_str_mv |
Bioinformática Computação de alto desempenho Biotecnologia Fluxo de trabalho Framework (Programa de computador) Dengue |
topic |
Bioinformática Computação de alto desempenho Biotecnologia Fluxo de trabalho Framework (Programa de computador) Dengue CNPQ::CIENCIAS BIOLOGICAS::BIOLOGIA GERAL |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS BIOLOGICAS::BIOLOGIA GERAL |
description |
In the last years, the development of technologies, such as next-generation sequencing and high-performance computing allowed the execution of Bioinformatics experiments of high complexity and computationally intensives. Different Bioinformatics fields need to use high-performance computing platforms to take advantage of the parallelism and tasks distribution, through specialized technologies of scientific workflows management systems. One of the Bioinformatics fields that need high-performance computing is phylogeny, a field that expresses the evolutive relations between genes and organisms, establishing which of them are most related evolutively. The phylogeny is used in several approaches, such as in the species classification; in the discovery of individuals’ kinship; in the identification of pathogens origins, and even in conservation biology. A way of representing these phylogenetic relations is using phylogenetic networks. However, the construction of these networks uses computationally intensive algorithms that require the constant manipulation of different input data. This work aims the development of a framework for construction of explicit phylogenetic networks, modeling a scientific workflow that adds different methods for the construction of the networks and the required input data treatment. The framework was developed to allow the use of multiple flows from the workflow in an automated, parallel, and distributed manner in a single execution and also to be executable in high- performance computing environments, constituting a challenging task, once the tools used are not developed focused in this environment. To orchestrate the workflow tasks, the scalable parallel programing library Parsl was used, allowing to do optimizations in the workflow’s tasks execution, performing better management of the resources. Two versions of the framework were developed, called Single Partition and Multi Partition, differing in the manner in which the resources are used. In tests performed, there was an improvement in the execution time of about five times when compared to the sequential execution of a flow without the optimizations. The framework was validated using public data of Dengue virus genomes, which were processed, annotated, and executed in the framework using the Santos Dumont supercomputer. The construction of the genomes’ explicit phylogenetic networks indicates that the framework is a functional, efficient, and easy to use tool. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022-02-18 |
dc.date.accessioned.fl_str_mv |
2023-04-18T14:57:05Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
TERRA, R. S. Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho. 2022. 71 f. Tese. (Programa de Pós-Graduação em Modelagem Computacional) - Laboratório Nacional de Computação Científica, Petrópolis, 2022. |
dc.identifier.uri.fl_str_mv |
https://tede.lncc.br/handle/tede/351 |
identifier_str_mv |
TERRA, R. S. Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho. 2022. 71 f. Tese. (Programa de Pós-Graduação em Modelagem Computacional) - Laboratório Nacional de Computação Científica, Petrópolis, 2022. |
url |
https://tede.lncc.br/handle/tede/351 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Laboratório Nacional de Computação Científica |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Modelagem Computacional |
dc.publisher.initials.fl_str_mv |
LNCC |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Coordenação de Pós-Graduação e Aperfeiçoamento (COPGA) |
publisher.none.fl_str_mv |
Laboratório Nacional de Computação Científica |
dc.source.none.fl_str_mv |
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LNCC |
institution |
LNCC |
reponame_str |
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Biblioteca Digital de Teses e Dissertações do LNCC |
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