Installing locally vs a container-based installation

We — and our collaborators — have set up different versions of nextstrain all across the world, on many different computer architectures. These experiences have resulted in two alternative approaches to installation, each with their pros & cons:

  1. “Local Install” which installs augur & auspice themselves, so that they’re available as individual programs on the command line. We highly recommend using conda to manage environments, however there are other options.
  2. “Container based installation” We provide a container-based system using docker, which includes all of the individual components behind nextstrain. You may then interact with this container through the nextstrain CLI (command line interface).

No matter which method you use, you’ll be able to run & modify pathogen builds on your own computer, and share the results through if you wish to.

It’s important to remember which installation method you choose, as the instructions in the tutorials will be slightly different depending on your installation approach!

Comparison of local vs container based installation

Comparison between the two methods:

We’ve found that the container-based method is generally better if you want to grab one of the pathogen builds and run it with your data. If you prefer to tinker with the methods or steps, or prefer your bioinformatics tools in a more Unix-y fashion, then a local installation may be the better choice.

I’m running nextstrain, how to I remember which installation method I used?

  • If you interact with things by running the nextstrain program, then you have the container based installation.
  • If you enable nextstrain through source activate nextstrain or conda activate nextstrain, and then use augur or auspice then you have a local install.

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