Please cite our paper:
Havrilla, J.M., Liu, C., Dong, X., Weng, C., Wang, K. PhenCards: a data resource linking human phenotype information to biomedical knowledge. Genome Med 13, 91 (2021). https://doi.org/10.1186/s13073-021-00909-8
Zenodo for Code:
Zenodo for Data:
This is the repository for the code used to make PhenCards.org
(C) Wang Lab 2020-2021
We have uploaded Docker images for PhenCards (don't forget to use 1.0.0 for paper version), Doc2Hpo, and Phen2Gene at: https://hub.docker.com/u/genomicslab. You can click the links to find them as well. You will also need the Docker image for Elasticsearch 7.8.1 which was used to build the Lucene indices and make the autocompletion and the site fast.
You need to set up certbot to get certificates to establish HTTPS for Doc2Hpo and communication with Phen2Gene and UMLS. You will need to use nginx or, as we did, httpd (Apache) to run services to create the site. Thanks to the docker-compose.yml file, running docker-compose build prod builds the production version of the site using the Dockerfile and the code there. Since you already have the docker images you can just run docker-compose up -d prod and it will run the Elasticsearch service and the Phen2Gene service for production. If you want to edit the code in dev mode and see how it affects the site, use docker-compose up -d app. And you can see it change in real time on the 5010 port. Production comes out the 5005 port. Elasticsearch runs on the 9300 and 9200 ports. Phen2Gene is locally run on the 6000 port and Doc2Hpo is run on the 7000 port. However, for your purposes, you can use https://phen2gene.wglab.org and https://doc2hpo.wglab.org for the services on the site. No real need to set up local Phen2Gene or Doc2Hpo. As stated below, you will need to run index_db.py initially on the data from Zenodo to create the Lucene index database once you have your Elasticsearch service running. Then you should not have to run it again. There are custom HTML, CSS, and JS templates for style on the site and these can be modified to your liking.
Make sure Python 3 is installed.
cd into the directory
run pip install -r requirements.txt
run python app.py
Go to localhost:5005 in your browser
If you would like to use debug mode when adjusting the features, run the following:
cd into the directory
do export FLASK_DEBUG=1 for Linux and Mac, or set FLASK_DEBUG=1 for Windows users
do FLASK run
Got to localhost:5000 in your browser, now you can monitor the changes in browser when changing the Flask code.
Additional note: use pip3 rather than pip since most systems have pip as part of python 2. To keep the server persistent, use nohup python3 app.py & to spin up the server.
The autocompletion feature is achieved by using elastic-search-7.8.1 on the backend and implemented using jquery-ui (esQuery.js) on the frondend.
Install and start elastic-search first: path-to-elastic-search/bin/elasticsearch, and then modified and executed index_db.py to index the database documents from https://zenodo.org/record/4755959.
To avoid any CORS header issues, adding the following two lines in path-to-elastic-search/config/elasticsearch.yml
http.cors.enabled : true
http.cors.allow-origin : "*"
More details can be found in https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters.html
Front-end files include templates/index.html, which is used to transfer input parameters from user; templates/results.html, which is used to generate result page with external links to other result pages inside templates folder. Another important part is templetes/templete.html, which is used for generate the overall templete of the whole front-end, other htmls are inheritated from this one.
Back-end files include API.py, which is used to connect with APIs and return formatted data structures; app.py is the high-level framework built for the app based on Flask; queries.py is used to execute local queries.