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Article: Incorporating heterogeneous sampling probabilities in continuous phylogeographic inference — Application to H5N1 spread in the Mekong region

TitleIncorporating heterogeneous sampling probabilities in continuous phylogeographic inference — Application to H5N1 spread in the Mekong region
Authors
Editors
Editor(s):Schwartz, R
Keywordsanimal
avian influenza
epidemic
Influenza A virus (H5N1)
phylogeny
Issue Date2020
PublisherOxford University Press (OUP): Policy B - Oxford Open Option B. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2020, v. 36 n. 7, p. 2098-2104 How to Cite?
AbstractMotivation: The potentially low precision associated with the geographic origin of sampled sequences represents an important limitation for spatially explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses. A substantial proportion of publicly available sequences is geo-referenced at broad spatial scale such as the administrative unit of origin, rather than more precise locations (e.g. geographic coordinates). Most frequently, such sequences are either discarded prior to continuous phylogeographic inference or arbitrarily assigned to the geographic coordinates of the centroid of their administrative area of origin for lack of a better alternative. Results: We here implement and describe a new approach that allows to incorporate heterogeneous prior sampling probabilities over a geographic area. External data, such as outbreak locations, are used to specify these prior sampling probabilities over a collection of sub-polygons. We apply this new method to the analysis of highly pathogenic avian influenza H5N1 clade data in the Mekong region. Our method allows to properly include, in continuous phylogeographic analyses, H5N1 sequences that are only associated with large administrative areas of origin and assign them with more accurate locations. Finally, we use continuous phylogeographic reconstructions to analyse the dispersal dynamics of different H5N1 clades and investigate the impact of environmental factors on lineage dispersal velocities. Availability and implementation: Our new method allowing heterogeneous sampling priors for continuous phylogeographic inference is implemented in the open-source multi-platform software package BEAST 1.10. Supplementary information: Supplementary data are available at Bioinformatics online.
Persistent Identifierhttp://hdl.handle.net/10722/290478
ISSN
2019 Impact Factor: 5.61
2015 SCImago Journal Rankings: 4.643
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorDellicour, S-
dc.contributor.authorLemey, P-
dc.contributor.authorArtois, J-
dc.contributor.authorLam, TY-
dc.contributor.authorFusaro, A-
dc.contributor.authorMonne, I-
dc.contributor.authorCattoli, G-
dc.contributor.authorKuznetsov, D-
dc.contributor.authorXenarios, I-
dc.contributor.authorDauphin, G-
dc.contributor.authorKalpravidh, W-
dc.contributor.authorVon Dobschuetz, S-
dc.contributor.authorClaes, F-
dc.contributor.authorNewman, SH-
dc.contributor.authorSuchard, MA-
dc.contributor.authorBaele, G-
dc.contributor.authorGilbert, M-
dc.contributor.editorSchwartz, R-
dc.date.accessioned2020-11-02T05:42:47Z-
dc.date.available2020-11-02T05:42:47Z-
dc.date.issued2020-
dc.identifier.citationBioinformatics, 2020, v. 36 n. 7, p. 2098-2104-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/290478-
dc.description.abstractMotivation: The potentially low precision associated with the geographic origin of sampled sequences represents an important limitation for spatially explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses. A substantial proportion of publicly available sequences is geo-referenced at broad spatial scale such as the administrative unit of origin, rather than more precise locations (e.g. geographic coordinates). Most frequently, such sequences are either discarded prior to continuous phylogeographic inference or arbitrarily assigned to the geographic coordinates of the centroid of their administrative area of origin for lack of a better alternative. Results: We here implement and describe a new approach that allows to incorporate heterogeneous prior sampling probabilities over a geographic area. External data, such as outbreak locations, are used to specify these prior sampling probabilities over a collection of sub-polygons. We apply this new method to the analysis of highly pathogenic avian influenza H5N1 clade data in the Mekong region. Our method allows to properly include, in continuous phylogeographic analyses, H5N1 sequences that are only associated with large administrative areas of origin and assign them with more accurate locations. Finally, we use continuous phylogeographic reconstructions to analyse the dispersal dynamics of different H5N1 clades and investigate the impact of environmental factors on lineage dispersal velocities. Availability and implementation: Our new method allowing heterogeneous sampling priors for continuous phylogeographic inference is implemented in the open-source multi-platform software package BEAST 1.10. Supplementary information: Supplementary data are available at Bioinformatics online.-
dc.languageeng-
dc.publisherOxford University Press (OUP): Policy B - Oxford Open Option B. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/-
dc.relation.ispartofBioinformatics-
dc.subjectanimal-
dc.subjectavian influenza-
dc.subjectepidemic-
dc.subjectInfluenza A virus (H5N1)-
dc.subjectphylogeny-
dc.titleIncorporating heterogeneous sampling probabilities in continuous phylogeographic inference — Application to H5N1 spread in the Mekong region-
dc.typeArticle-
dc.identifier.emailLam, TY: ttylam@hku.hk-
dc.identifier.authorityLam, TY=rp01733-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1093/bioinformatics/btz882-
dc.identifier.pmid31790143-
dc.identifier.pmcidPMC7141868-
dc.identifier.scopuseid_2-s2.0-85083072540-
dc.identifier.hkuros317734-
dc.identifier.volume36-
dc.identifier.issue7-
dc.identifier.spage2098-
dc.identifier.epage2104-
dc.publisher.placeUnited Kingdom-

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