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    "info": {
        "author": "M\u00e5ns Magnusson",
        "author_email": "mans.magnusson@scilifelab.se",
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            "Environment :: Web Environment",
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            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
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        "description": "\n<p align=\"center\">\n\t<a href=\"http://www.clinicalgenomics.se/scout\">\n\t\t<img height=\"165\" width=\"637\" src=\"artwork/logo-display.png\">\n\t</a>\n\t<h3 align=\"center\">Analyze VCFs and collaborate on solving rare diseases quicker</h3>\n</p>\n\n[![Build Status][travis-img]][travis-url]\n[![PyPI Version][pypi-img]][pypi-url]\n[![Coverage Status](https://coveralls.io/repos/github/Clinical-Genomics/scout/badge.svg?branch=master)](https://coveralls.io/github/Clinical-Genomics/scout?branch=master)\n\n## What is Scout?\n\n- **Simple** - Analyze variants in a simple to use web interface.\n- **Aggregation** - Combine results from multiple analyses and VCFs into a centralized database.\n- **Collaboration** - Write comments and share cases between users and institutes.\n\n## Documentation\n\nThis 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/).\n\n## Installation\n\n<!-- You can install the latest release of Scout using `pip`:\n\n```bash\npip install scout-browser\n\n# ... to include optional coverage tools you would use:\npip install scout-browser[coverage]\n```\n\nIf you would like to install Scout for local development: -->\n\n```bash\ngit clone https://github.com/Clinical-Genomics/scout\ncd scout\npip install --requirement requirements.txt --editable .\n```\n\nScout PDF reports are created using [Flask-WeasyPrint](https://pythonhosted.org/Flask-WeasyPrint/). This library requires external dependencies which need be installed separately (namely Cairo and Pango). See platform-specific instructions for Linux, macOS and Windows available on the WeasyPrint installation [pages](https://weasyprint.readthedocs.io/en/stable/install.html#).\n\nYou also need to have an instance of MongoDB running. I've found that it's easiest to do using the official Docker image:\n\n```bash\ndocker run --name mongo -p 27017:27017 mongo\n```\n\n## Usage\n\n### Demo\n\nOnce 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:\n\n```bash\nscout setup demo\n```\n\nThis will setup an instance of scout with a database called `scout-demo`. Now run\n\n```bash\nscout --demo serve\n```\nAnd play around with the interface. A user has been created with email clark.kent@mail.com so use that adress to get access\n\n### Initialize scout\n\nTo initialize a working instance with all genes, diseases etc run\n\n```bash\nscout setup database\n```\n\nfor more info, run `scout --help`\n\n> If you intent to use authentication, make sure you are using a Google email!\n\nThe 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:\n\n```bash\nscout load panel scout/demo/panel_1.txt\nscout load case scout/demo/643594.config.yaml\n```\n\n## Integration with chanjo for coverage report visualization\n\nScout 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).\n\n## Server setup\n\nScout 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.\n\nThis is an example of the config file:\n\n```python\n# scoutconfig.py\n\n# list of email addresses to send errors to in production\nADMINS = ['paul.anderson@magnolia.com']\n\nMONGO_HOST = 'localhost'\nMONGO_PORT = 27017\nMONGO_DBNAME = 'scoutTest'\nMONGO_USERNAME = 'testUser'\nMONGO_PASSWORD = 'testPass'\n\n# enable user authentication using Google OAuth\nGOOGLE = dict(\n\t\tconsumer_key='CLIENT_ID',\n\t\tconsumer_secret='CLIENT_SECRET',\n\t\tbase_url='https://www.googleapis.com/oauth2/v1/',\n\t\tauthorize_url='https://accounts.google.com/o/oauth2/auth',\n\t\trequest_token_url=None,\n\t\trequest_token_params={\n\t\t\t\t'scope': (\"https://www.googleapis.com/auth/userinfo.profile \"\n\t\t\t\t\t\t  \"https://www.googleapis.com/auth/userinfo.email\"),\n\t\t},\n\t\taccess_token_url='https://accounts.google.com/o/oauth2/token',\n\t\taccess_token_method='POST'\n)\n\n# enable Phenomizer gene predictions from phenotype terms\nPHENOMIZER_USERNAME = '???'\nPHENOMIZER_PASSWORD = '???'\n\n# enable Chanjo coverage integration\nSQLALCHEMY_DATABASE_URI = '???'\nREPORT_LANGUAGE = 'en'  # or 'sv'\n\n# other interesting settings\nSQLALCHEMY_TRACK_MODIFICATIONS = False  # this is essential in production\nTEMPLATES_AUTO_RELOAD = False  \t\t\t# consider turning off in production\nSECRET_KEY = 'secret key'               # override in production!\n```\n\nStarting the server in now really easy, for the demo and local development we will use the CLI:\n\n```bash\nscout serve --config ./config.py\n```\n\n![Scout Interface demo](artwork/scout-variant-demo.png)\n\n### Hosting a production server\n\nWhen 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.\n\n```bash\nSCOUT_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\n```\n\n> The `wsgi_gunicorn.py` file is included in the repo and configures Flask to work with Gunicorn.\n\n\n### Integration with MatchMaker Exchange\n\nStarting from release 4.4, Scout offers integration for patient data sharing via MatchMaker Exchange. General info about MatchMaker and patient matching could be found in [this paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016856/).  \nFor a technical guideline of our implementation of MatchMaker Exchange at Clinical Genomics and its integration with Scout follow this [link](docs/admin-guide/matchmaker_exchange_integration.md).  \nA user-oriented guide describing how to share case and variant data to MatchMaker using Scout can be found [here](docs/user-guide/cases.md#matchmaker-exchange-integration).\n\n\n## Example of analysis config\n\nTODO.\n\n\n### Contributing to Scout\n\nIf you want to contribute and make Scout better, you help is very appreciated! 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