As of mid-October 2017, MegaQC has all basic functionality. We've made the repo public, but please bear in mind that it is still under heavy development and changes are being made on a daily basis. It's safe to assume that the database structure is still at risk and that you shouldn't yet trust it to be stable. However, we'd love your help in testing, bug finding and development!
MegaQC is a web application that you can install and run on your own network. It collects and visualises data parsed by MultiQC across multiple runs.
Once MegaQC is installed and running, simply configure MultiQC to automatically save data to the website every time it runs (find instructions in the running MegaQC website). Users of your group or facility can then replicate MultiQC plots and explore different data fields. Data distributions, timelines and comparisons can all be explored.
The MegaQC homepage looks something like this:
If you're not sure what MultiQC is yet, check out the main MultiQC website and GitHub repo first.
MegaQC has been written in Python using the Flask web framework. MegaQC is designed to be very simple to get up and running for basic testing and evaluation, yet super easy to configure for a high performance production installation.
By default, MegaQC installs with configuration to use the Flask development server and a SQLite database. This allows a very simple pure-Python installation where you can get up and running almost immediately.
MegaQC is much slower in this testing mode than with a proper production installation, so don't be too quick to judge it as being slow!
If you would like the development version instead, the command is:
pip install --upgrade --force-reinstall git+https://github.com/ewels/MegaQC.gitOnce installed, run the server with the following command:
megaqc run
The flask server is single-threaded, meaning that only one person can load a page or a plot at a time. The SQLite database works using flat files on the disk and much slower than fully fledged SQL databases. As such, it should not be used in production and will run slowly during testing.
Once happy with MegaQC, you can should run it in production will a multi-threaded server application and high performance database. MegaQC is designed to be simply run with Postgres SQL and Gunicorn server, however you're not tied to these - the site is written with SQL-Alchemy and should work with most SQL database types and Flask works with most server architectures.
MegaQC comes with instructions for how to run an installation for Gunicorn + Apache with Postgres SQL. It also comes with a Docker container for super-fast setup (note that all data will be lost if the docker container is stopped by default).
Please see the docs for full instructions for each method.
Contributions and suggestions for new features are welcome, as are bug reports! Please create a new issue for any of these, including example reports where possible.
There is a chat room for the package hosted on Gitter where you can discuss things with the package author and other developers: https://gitter.im/ewels/MegaQC
If in doubt, feel free to get in touch with the main author directly: @ewels ([email protected])
See all contributors on GitHub.