Interactive Bioinformatics Visualization using Auspice

Visualization of bioinformatics results is an integral part of current phylodynamics, both for data exploration and communication. We wanted to build a tool that was highly interactive, versatile and usable as communication platform to quickly disseminate results to the wider community. Auspice is written in JavaScript and is the app that powers all the phylogenomic analysis on The code is completely open source and may be found on Github).

mumps Auspice displaying Mumps genomes from North America. See

General design overview

We wanted to build a powerful yet not overly complex visualization tool. Currently this is centered around a number of “panels”. These allow us to display relationships between isolates such as their phylogenetic relationships, putative transmissions on the map, variability across the genome. Color is used consistently throughout the app in order to link different panels. The generator of the data controls which traits are able to be visualized - for instance, transmissions can be turned off if the data is not informative. A number of controls are made available in a sidebar to control the time period viewed, the layout of the tree etc.

We are currently working on allowing scientists to author custom narratives which describe the data, and control how the data is visualized as one progresses through the narrative. See here for more information.

Auspice is agnostic about where the data came from

We build bioinformatic tooling (augur, docs here) to produce JSONs specifically for visualization in Auspice. However any compatible JSONs can be visualized through auspice — either locally, or via (see below). The data doesn’t have to be viral genomes, or real-time, or generated in Augur!

We’re working on adding tutorials on how to convert BEAST results etc into the formats used by Auspice. In the meantime, the JSON file formats are specified here.

Running locally

Auspice can be run locally to visualize datasets. See local installation for how to get up and running.

Private (non public) datasets

We are looking to include logins / accounts through in order to serve private datasets, but this feature is not currently available.

All source code is freely available under the terms of the GNU Affero General Public License. Screenshots etc may be used as long as a link to is provided.

This work is made possible by the open sharing of genetic data by research groups from all over the world. We gratefully acknowledge their contributions. Special thanks to Kristian Andersen, Allison Black, David Blazes, Peter Bogner, Matt Cotten, Ana Crisan, Gytis Dudas, Vivien Dugan, Karl Erlandson, Nuno Faria, Jennifer Gardy, Becky Garten, Dylan George, Ian Goodfellow, Nathan Grubaugh, Betz Halloran, Christian Happi, Jeff Joy, Paul Kellam, Philippe Lemey, Nick Loman, Sebastian Maurer-Stroh, Louise Moncla, Oliver Pybus, Andrew Rambaut, Colin Russell, Pardis Sabeti, Katherine Siddle, Kristof Theys, Dave Wentworth, Shirlee Wohl and Nathan Yozwiak for comments, suggestions and data sharing.


© 2015-2019 Trevor Bedford and Richard Neher