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StrainHub: a phylogenetic tool to construct pathogen transmission networks
Author(s) -
Adriano de Bernardi Schneider,
Colby T. Ford,
Reilly Hostager,
J. R. Williams,
Michael Cioce,
Ümit V. Çatalyürek,
Joel O. Wertheim,
Daniel Janies
Publication year - 2019
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz646
Subject(s) - phylogenetic tree , metadata , centrality , tree (set theory) , betweenness centrality , computer science , phylogenetic network , isolation (microbiology) , metric (unit) , theoretical computer science , transmission (telecommunications) , data mining , biology , bioinformatics , world wide web , mathematics , genetics , statistics , mathematical analysis , telecommunications , operations management , gene , economics
In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g. host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in metadata, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis.

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