Phylogeny and Metadata Network Database for Epidemiologic Surveillance
Published in BioRxiv, 2022
The ongoing SARS-CoV-2 pandemic has highlighted the difficulty in integrating disparate data sources for epidemiologic surveillance. To address this challenge, we have created a graph database to integrate phylogenetic trees, associated metadata, and community surveillance data for phylodynamic inference. As an example use case, we divided 22,713 SARS-CoV-2 samples into 5 groups, generated maximum likelihood trees, and inferred a potential transmission network from a forest of minimum spanning trees built on patristic distances between samples. We then used Cytoscape to visualize the resultant graphs. Download paper here
Recommended citation:
Garrick Stott, Leke Lyu, Gabriella Veytsel, Jacky Kuo, Ryan Lewis, Armand Brown, Kayo Fujimoto, Justin Bahl. Phylogeny and Metadata Network Database for Epidemiologic Surveillance. bioRxiv 2022.04.19.488067. https://doi.org/10.1101/2022.04.19.488067
Recommended citation: Garrick Stott, Leke Lyu, Gabriella Veytsel, Jacky Kuo, Ryan Lewis, Armand Brown, Kayo Fujimoto, Justin Bahl. Phylogeny and Metadata Network Database for Epidemiologic Surveillance. bioRxiv 2022.04.19.488067
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