Nextstrain: analysis and visualization of pathogen sequence data

Nextstrain is an open-source project to harness the scientific and public health potential of pathogen genome data. We provide a continually-updated view of publicly available data with powerful analytics and visualizations showing pathogen evolution and epidemic spread. Our goal is to aid epidemiological understanding and improve outbreak response. If you have any questions, or simply want to say hi, please give us a shout at

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What is Nextstrain? aims to provide a real-time snapshot of evolving pathogen populations and to provide interactive data visualizations to virologists, epidemiologists, public health officials and citizen scientists. Through interactive data visualizations, we aim to allow exploration of continually up-to-date datasets, providing a novel surveillance tool to the scientific and public health communities.

In the process we have created a number of open-source tools (described above) which have allowed a growing community to produce similar analyses, and we want to promote this community through nextstrain. Our model for data analysis and sharing is for scientists to store the code used for their analysis in GitHub repositories, and if the results are also stored in these repositories they are automatically made available through URLs (see here for more details).

Open source tools for the community

Nextstrain is a collection of open-source tools to aid in our understanding of pathogen spread and evolution, especially in outbreak scenarios. We have designed these in such a way that they can be used with a wide range of data sources, and are easy to replace with your own tooling. Broadly speaking, Nextstrain consists of

  • “augur” — a series of composable, modular (Unix-like) bioinformatics tools. We use these to create recipes for different pathogens and different analyses, which are easy to reproduce when new data is available.
  • “auspice” — a web-based visualization program, to present & interact with phylogenomic & phylogeographic data. This is what you see when, for example, you visit, but it can also run locally on your computer.

This architecture allows us to

  • perform sequence analysis — including subsampling, alignment, tree-inference, node dating et cetera — by running a series of augur commands in discrete steps.
  • use additional tools & scripts within a given bioinformatics recipe to add additional functionality.
  • replace modules, or series of modules with other analysis tools (e.g. BEAST).
  • interpret our data — no matter what analysis recipe we used — within auspice on our computer.
  • share our results to collaborators or other scientists through
  • rerun analysis as new data become available

We use these tools to provide a continually-updated view of publicly available data for certain important pathogens such as influenza, Ebola and Zika viruses. These data are continually updated whenever new genomes are made available, thus providing the most up-to-date view possible.

More information:


If pathogen genome sequences are going to inform public health interventions, then analyses have to be rapidly conducted and results widely disseminated. Current scientific publishing practices hinder the rapid dissemination of epidemiologically relevant results. We thought an open online system that implements robust bioinformatic pipelines to synthesize data from across research groups has the best capacity to make epidemiologically actionable inferences. Additionally we have open-sourced all the tools we use, and hope to create a community around nextstrain which supports and promotes genomic analyses of various kinds.

How to get started

  • If you would like to investigate live datasets — including those contributed by the community, head back to the splash page and click on any of the tiles.
  • If you would like to use Nextstrain to process and visualize your own data, you can either start with the Quickstart which uses a Docker container to run the builds automatically, or follow the Zika Tutorial which provides a more hands-on approach to processing the data.
  • If you have data generated from other sources (e.g. BEAST, RAxML, etc…) then please watch this space — we’ll add tutorials for these soon!

Contact us

We are keen to keep expanding the scope of Nextstrain and empowering other researchers to better analyze and understand their data. Please get in touch with us if you have any questions or comments.


If you use, augur or auspice as part of your analysis, please cite 👇👇

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