Welcome to StrainHub, an open access web-based software to generate disease transmission networks and associated metrics from a combination of a phylogenetic tree and associated metadata.
StrainHub is being developed as a collaborative project between researchers from the University of California at San Diego and the University of North Carolina at Charlotte as an effort to create new the tools that will enable an in-depth analysis and data visualization of the spread of pathogens.
Table of Contents
About StrainHub
StrainHub is designed as a web-based software to generate pathogen transmission networks and associated metrics from a combination of a phylogenetic tree and a metadata associated. The software enables the construction of the network by either mapping the metadata onto the tree and performing a parsimony ancestry reconstruction step to create links between the associated metadata or parsing a BEAST phylogeography maximum clade credibility tree.
Getting Started
In order to construct the disease transmission network the user is presented with two options, run the entire pipeline or skip straight to the visualization part.
To run the entire pipeline, two files will need to be generated.
1) A phylogenetic tree formatted in Newick tree format generated through your preferred phylogenetic search method (e.g., BEAST, TNT, RAxML, IQTree). Alternatively, you can use an aligned multiple sequence file in FASTA format.
2) A metadata associated file formatted as a comma separated value (CSV) file that includes headers, has the Accession number as the first column and the metadata associated values (e.g., host, country, risk group)
If you would like to visualize the distribution of your pathogen in a map, a third optional geodata file need to be generated with all locations and geographic coordinates.
Taxa ID with missing data should be excluded prior to the analysis. Order does not matter on both files, the metadata is sorted automatically before mapped into the tree based on the header of the first column of the CSV file (Accession).
A phylogenetic tree in Newick format is required to perform the Parsimony Ancestral Reconstruction (“Parsimony” method). If you elect to utilize a multiple sequence alignment instead of your tree, you need to run the alignment through the “Create Neighbor-Joining Tree” method to generate a tree which will be fed into the Parsimony Ancestral Reconstruction pipeline.
A template for building the metadata file can be downloaded here. Do not change the header of the Accession column as it is necessary to identify and reorder the metadata according to the tree file.
Example files:
To skip the ancestry reconstruction step, the user will have to run BEAST phylogeography, and then one file will need to be generated.
1) A maximum clade credibility tree (MCC) generated through Phylogeographic Diffusion in Discrete or Continuous Space in BEAST in NEXUS format.
Our app will parse the MCC tree and prompt the user to select which trait he wants to evaluate. Once the user has selected the trait, the user will have to select the trait probability threshold the user wants to use to consider the relationship between two nodes valid (we like 0.9 as a threshold, but this will depend on the user and his knowledge about his dataset).
The threshold set by the user is utilized to filter out any pair of nodes within the phylogenetic tree that may have lower probability, thus being ignored within the transmission network step.
Example file:
Note: To download example files, right click on the link and save file.
Metrics
The nodes within the network are scaled based on the user selected metric. The current metrics available on StrainHub are:
Betweenness Centrality
Betweenness centrality of node i is the sum of all the ratios between all the paths g from node j to node k that go through node i over all possible paths from node j to node k.
Epidemiological meaning: How important a location/host is as the shortest path / intermediary connecting other locations/hosts within the transmission network.
Closeness Centrality
Closeness centrality is the inverse of the average distance between pk and the other nodes.
Epidemiological meaning: How important a location/host is given its distance within the transmission network to other locations/hosts.
Degree Centrality
Degree centrality of node k is the sum of all links of other nodes to node k over the total number of nodes – 1.
Epidemiological meaning: How important a location/host is within the transmission network given the number of times that a disease emerges from or to that point (indegree or outdegree).
Degree Centrality is also subdivided in Indegree and Outdegree Centrality, which calculate the links directed only in or out of the note, respectively.
Source Hub Ratio (SHR)
The Source Hub Ratio of a node i is the sum of all shifts from location “i” to other locations over the sum of all shifts from and to location ”i”.
Epidemiological meaning: How important a node is within the network as source of the disease, ignoring centrality within network.
1 = all shifts originated on node i (source) 0.5 = same amount of shifts from and to node i (hub) 0 = all shifts occurred to node i (dead end)
Although only one metric can be selected for scaling the nodes within the network, all the metrics are calculated and can be visualized as well as downloaded on the tab “Metrics”.
Network Quality
The overall quality of the transmission network generated on StrainHub rests on the quality of the user input data.
How To Cite
Please cite the reference below if you are using StrainHub.
Text Citation:
Adriano de Bernardi Schneider, Colby T Ford, Reilly Hostager, John Williams, Michael Cioce, Ümit V Çatalyürek, Joel O Wertheim, Daniel Janies, StrainHub: A phylogenetic tool to construct pathogen transmission networks, Bioinformatics, btz646, https://doi.org/10.1093/bioinformatics/btz646
BibTex Citation:
@article{10.1093/bioinformatics/btz646,
author = {de Bernardi Schneider, Adriano and Ford, Colby T and Hostager, Reilly and Williams, John and Cioce, Michael and Çatalyürek, Ümit V and Wertheim, Joel O and Janies, Daniel},
title = "{StrainHub: A phylogenetic tool to construct pathogen transmission networks}",
journal = {Bioinformatics},
year = {2019},
month = {08},
abstract = "{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.strainhub.io and https://github.com/abschneider/StrainHub}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz646},
url = {https://doi.org/10.1093/bioinformatics/btz646},
eprint = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz646/29171171/btz646.pdf},
}
Additionally, if you use the Map feature of StrainHub please also cite the reference below:
Text Citation:
Schneider, A.D.B., Ochsenreiter, R., Hostager, R., Hofacker, I.L., Janies, D. and Wolfinger, M.T., 2019. Updated Phylogeny of Chikungunya Virus Suggests Lineage-Specific RNA Architecture. Viruses, 11(9), p.798.
BibTex Citation:
@article{schneider2019updated,
title={Updated Phylogeny of Chikungunya Virus Suggests Lineage-Specific RNA Architecture},
author={de Bernardi Schneider, Adriano and Ochsenreiter, Roman and Hostager, Reilly and Hofacker, Ivo L and Janies, Daniel and Wolfinger, Michael T},
journal={Viruses},
volume={11},
number={9},
pages={798},
year={2019},
publisher={Multidisciplinary Digital Publishing Institute}
}
Authors
Adriano de Bernardi Schneider, Ph.D.
Colby T. Ford, Ph.D.
Reilly Hostager
John Williams
Michael Cioce
Ümit V. Çatalyürek, Ph.D.
Joel O. Wertheim, Ph.D.
Daniel Janies, Ph.D.
References
Contact
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