Clinical DNA variant visualizer and browser.
<img height="165" width="637" src="artwork/logo-display.png">
<h3 align="center">Analyze VCFs and collaborate on solving rare diseases quicker</h3>
## What is Scout?
- **Simple** - Analyze variants in a simple to use web interface.
- **Aggregation** - Combine results from multiple analyses and VCFs into a centralized database.
- **Collaboration** - Write comments and share cases between users and institutes.
This README only gives a brief overview of Scout, for a more complete reference, please check out our docs: [www.clinicalgenomics.se/scout](http://www.clinicalgenomics.se/scout/).
<!-- You can install the latest release of Scout using `pip`:
pip install scout-browser
# ... to include optional coverage tools you would use:
pip install scout-browser[coverage]
If you would like to install Scout for local development: -->
git clone https://github.com/Clinical-Genomics/scout
pip install --requirement requirements.txt --editable .
You also need to have an instance of MongoDB running. I've found that it's easiest to do using the official Docker image:
docker run --name mongo -p 27017:27017 mongo
Once installed, you can setup Scout by running a few commands using the included command line interface. Given you have a MongoDB server listening on the default port (27017), this is how you would setup a fully working Scout demo:
scout setup demo
This will setup an instance of scout with a database called `scout-demo`. Now run
scout --demo serve
And play around with the interface. A user has been created with email email@example.com so use that adress to get access
### Initialize scout
To initialize a working instance with all genes, diseases etc run
scout setup database
for more info, run `scout --help`
> If you intent to use authentication, make sure you are using a Google email!
The previous command setup the database with a curated collection of gene definitions with links to OMIM along with HPO phenotype terms. Now we will load some example data. Scout expects the analysis to be accomplished using various gene panels so let's load one and then our first analysis case:
scout load panel scout/demo/panel_1.txt
scout load case scout/demo/643594.config.yaml
## Integration with chanjo for coverage report visualization
Scout may be configured to visualize coverage reports produced by [Chanjo][chanjo]. Instructions on how to enable this feature can be found in the document [chanjo_coverage_integration](docs/admin-guide/chanjo_coverage_integration.md).
## Server setup
Scout needs a server config to know which databases to connect to etc. Depending on which information you provide you activate different parts of the interface automatically, including user authentication, coverage, and local observations.
This is an example of the config file:
# list of email addresses to send errors to in production
ADMINS = ['firstname.lastname@example.org']
MONGO_HOST = 'localhost'
MONGO_PORT = 27017
MONGO_DBNAME = 'scoutTest'
MONGO_USERNAME = 'testUser'
MONGO_PASSWORD = 'testPass'
# enable user authentication using Google OAuth
GOOGLE = dict(
'scope': ("https://www.googleapis.com/auth/userinfo.profile "
# enable Phenomizer gene predictions from phenotype terms
PHENOMIZER_USERNAME = '???'
PHENOMIZER_PASSWORD = '???'
# enable Chanjo coverage integration
SQLALCHEMY_DATABASE_URI = '???'
REPORT_LANGUAGE = 'en' # or 'sv'
# other interesting settings
SQLALCHEMY_TRACK_MODIFICATIONS = False # this is essential in production
TEMPLATES_AUTO_RELOAD = False # consider turning off in production
SECRET_KEY = 'secret key' # override in production!
Starting the server in now really easy, for the demo and local development we will use the CLI:
scout serve --config ./config.py
![Scout Interface demo](artwork/scout-variant-demo.png)
### Hosting a production server
When running the server in production you will likely want to use a proper Python server solution such as Gunicorn. This is also how we can multiprocess the server and use encrypted HTTPS connections.
SCOUT_CONFIG=./config.py gunicorn --workers 4 --bind 0.0.0.0:8080 --access-logfile - --error-logfile - --keyfile /tmp/myserver.key --certfile /tmp/server.crt wsgi_gunicorn:app
> The `wsgi_gunicorn.py` file is included in the repo and configures Flask to work with Gunicorn.
## Example of analysis config
Release history Release notifications
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|scout_browser-4.0.0-py2.py3-none-any.whl (2.7 MB) Copy SHA256 hash SHA256||Wheel||py2.py3||Jun 15, 2018|
|scout-browser-4.0.0.tar.gz (2.6 MB) Copy SHA256 hash SHA256||Source||None||Jun 15, 2018|