Fast phylogenetic inference from typing data

Detalhes bibliográficos
Autor(a) principal: Carrico, Joao
Data de Publicação: 2018
Outros Autores: Crochemore, Maxime, Francisco, Alexandre, Pissis, Solon, Gonçalves, Bruno Ribeiro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/8431
Resumo: Background: Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequencebased typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. Results: We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.
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spelling Fast phylogenetic inference from typing dataComputational biologyPhylogenetic inferenceHamming distanceBackground: Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequencebased typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. Results: We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.BMCRCIPLCarrico, JoaoCrochemore, MaximeFrancisco, AlexandrePissis, SolonGonçalves, Bruno Ribeiro2018-04-23T09:24:05Z2018-02-152018-02-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/8431engCARRIÇO, João A.; [et al] – Fast phylogenetic inference from typing data. Algorithms for Molecular Biology. ISSN 1748-7188. Vol. 13 (2018), pp. 1-141748-718810.1186/s13015-017-0119-7info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T09:55:51Zoai:repositorio.ipl.pt:10400.21/8431Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:17:11.907282Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Fast phylogenetic inference from typing data
title Fast phylogenetic inference from typing data
spellingShingle Fast phylogenetic inference from typing data
Carrico, Joao
Computational biology
Phylogenetic inference
Hamming distance
title_short Fast phylogenetic inference from typing data
title_full Fast phylogenetic inference from typing data
title_fullStr Fast phylogenetic inference from typing data
title_full_unstemmed Fast phylogenetic inference from typing data
title_sort Fast phylogenetic inference from typing data
author Carrico, Joao
author_facet Carrico, Joao
Crochemore, Maxime
Francisco, Alexandre
Pissis, Solon
Gonçalves, Bruno Ribeiro
author_role author
author2 Crochemore, Maxime
Francisco, Alexandre
Pissis, Solon
Gonçalves, Bruno Ribeiro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Carrico, Joao
Crochemore, Maxime
Francisco, Alexandre
Pissis, Solon
Gonçalves, Bruno Ribeiro
dc.subject.por.fl_str_mv Computational biology
Phylogenetic inference
Hamming distance
topic Computational biology
Phylogenetic inference
Hamming distance
description Background: Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequencebased typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. Results: We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.
publishDate 2018
dc.date.none.fl_str_mv 2018-04-23T09:24:05Z
2018-02-15
2018-02-15T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.21/8431
url http://hdl.handle.net/10400.21/8431
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv CARRIÇO, João A.; [et al] – Fast phylogenetic inference from typing data. Algorithms for Molecular Biology. ISSN 1748-7188. Vol. 13 (2018), pp. 1-14
1748-7188
10.1186/s13015-017-0119-7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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