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Fast phylogenetic inference from typing data

dc.contributor.authorCarrico, Joao
dc.contributor.authorCrochemore, Maxime
dc.contributor.authorFrancisco, Alexandre
dc.contributor.authorPissis, Solon
dc.contributor.authorRibeiro-Gonçalves, Bruno
dc.contributor.authorVaz, Cátia
dc.date.accessioned2021-05-10T10:54:24Z
dc.date.available2021-05-10T10:54:24Z
dc.date.issued2018-02-15
dc.description.abstractBackground: Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profle 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 diferent profles. On the other hand, computing genetic evolution ary distances among a set of typing profles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance defnitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profles. 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCARRIÇO, João A.; [et al] – Fast phylogenetic inference from typing data. Algorithms for Molecular Biology. ISSN 1748-7188. Vol. 13 (2018), pp. 1-14pt_PT
dc.identifier.doi10.1186/s13015-017-0119-7pt_PT
dc.identifier.issn1748-7188
dc.identifier.urihttp://hdl.handle.net/10400.21/13315
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationTUBITAK/0004/2014 - FCT / Scientifc and Technological Research Council of Turkey (Türkiye Bilimsel ve Teknolojik Araşrrma Kurumu, TÜBİTAK)pt_PT
dc.relationLISBOA-01-0145-FEDER-016394 - PRECISE co-funded by FEEIpt_PT
dc.relationLISBOA-01-0145-FEDER-016417 - ONEIDA co-funded by FEEIpt_PT
dc.relationUID/CEC/500021/2013 - FCT and INNUENDO project [25] co-funded by the European Food Safety Authority (EFSA), grant agreement GP/EFSA/ AFSCO/2015/01/CT2pt_PT
dc.relation.publisherversionhttps://almob.biomedcentral.com/track/pdf/10.1186/s13015-017-0119-7.pdfpt_PT
dc.subjectComputational biologypt_PT
dc.subjectPhylogenetic inferencept_PT
dc.subjectHamming distancept_PT
dc.titleFast phylogenetic inference from typing datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage14pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleAlgorithms for Molecular Biologypt_PT
oaire.citation.volume13pt_PT
person.familyNameCarrico
person.familyNameCrochemore
person.familyNameFrancisco
person.familyNamePissis
person.familyNameVaz
person.givenNameJoao
person.givenNameMaxime
person.givenNameAlexandre
person.givenNameSolon
person.givenNameCátia
person.identifier.ciencia-id8718-741E-BBD9
person.identifier.orcid0000-0002-5274-2722
person.identifier.orcid0000-0003-1087-1419
person.identifier.orcid0000-0003-4852-1641
person.identifier.orcid0000-0002-1445-1932
person.identifier.orcid0000-0001-6074-3074
person.identifier.ridA-7367-2008
person.identifier.ridK-2041-2017
person.identifier.ridADC-1473-2022
person.identifier.scopus-author-id8266333200
person.identifier.scopus-author-id35576964400
person.identifier.scopus-author-id35180253600
person.identifier.scopus-author-id27267941600
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscoveryc151b29d-016d-448b-82e7-4c012e300ee2

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