pharos-community-data-api

A test API to be used for Pharos' dynamic predictions functionality.

Tutorial

Build and Run this code

1. Fork this repo from the GitHub UI
   • 
   • Select your account as the owner
2. Check out your fork locally

   git clone https://github.com/{{your github ID}}/pharos-community-data-api
   

3. Navigate to source code directory

   cd /pharos-community-data-api
   

4. Install all dependencies

   npm install
   

5. OPTIONAL - Build Kinase-Cancer-Predictions endpoint
   • download MESH IDs

     curl https://nlmpubs.nlm.nih.gov/projects/mesh/MESH_FILES/xmlmesh/desc2023.xml > desc2023.xml
     

   • build lookup tables for Kinase-Cancer Predictions

     cd data_sources/kinase-cancer-predictions
     node build
     cd ../..
     

6. THE OTHER OPTION - Skip building Kinase-Cancer-Predictions endpoint
   • Alternatively, you can comment out the following lines from app.ts

   import {predictions} from "./data_sources/kinase-cancer-predictions/index"
   ...
   app.get("/predictions?*", (req: Request, res: Response) => predictions(req, res));
   

7. Install typescript if you haven't already

   npm install -g typescript
   

8. Compile the typescript and run the app

   tsc && node app
   

9. Verify it works.
   • Navigate to:

     http://localhost:3001/ping
     

   • it should return some JSON
10. Hit your API from the Pharos Development deployment
    • Navigate to:

    https://pharos-frontend-dev.appspot.com/toolbox
    

    • Choose a target, disease, or ligand from the search box
    • enter your API into the API Url field
    • add some query parameters
    • add some fields in curly braces (i.e. {name}) to pass data from Pharos to your API

    http://localhost:3001/ping?name={name}&message=Hello world
    

    • see the preview of how the API results will look on a Pharos details page

Add your own endpoint

Here are the steps for creating your own endpoint. As an example, it downloads data from Alliance Genome for display on Disease Details pages. See the GitHub branch called "tutorial" for finished example for reference (note, you'd still have to download the data and build the lookup file to run the API).

1. Download target-disease associations from Alliance Genome and unzip it in a new folder ./data_sources/alliance-genome

   mkdir ./data_sources/alliance-genome
   curl https://download.alliancegenome.org/5.3.0/DISEASE-ALLIANCE-JSON/HUMAN/DISEASE-ALLIANCE-JSON_HUMAN_25.json.gz > ./data_sources/alliance-genome/disease-alliance-json_human_25.json.gz
   gunzip ./data_sources/alliance-genome/disease-alliance-json_human_25.json.gz
   

2. Write a build script to format the data for easy access

   • in the /data_sources/alliance-genome folder, create a file called build.ts
   • at the top of build.ts, read the unzipped input file

   const rawData = require('./disease-alliance-json_human_25.json');
   

   • Next, build a map so that we can access the data given a gene symbol
     • gene symbol is held in the DBObjectSymbol field of each object in the data array
     • iterate through the data in rawData.data and build a Map object holding the relevant data

   const targetLookup: Map<string, any[]> = new Map<string, any[]>();
   rawData.data.forEach((row: any) => {
       let associations: any = [];
       if (targetLookup.has(row.DBObjectSymbol)) {
           associations = targetLookup.get(row.DBObjectSymbol);
       } else {
           targetLookup.set(row.DBObjectSymbol, associations);
       }
       associations.push({
           type: row.AssociationType, // i.e. is_marker_for, is_implicated_in, etc.
           diseaseID: row.DOID,
           diseaseTerm: row.DOtermName,
           evidenceCode: row.EvidenceCode,
           evidenceCodeName: row.EvidenceCodeName,
           reference: row.Reference // can be PMID, RGD, MGI
       });
   });
   

   • Save targetLookup.get(geneSymbol) to a file so that the API can load it and it will be ready to fetch the data when a request for diseases associated with a particular gene symbol

   const { replacer } = require("../kinase-cancer-predictions/utilities");
   fs.writeFileSync(__dirname + '/targetLookup.json', JSON.stringify(targetLookup, replacer));
   

3. Compile the typescript code, and run the build script to create targetLookup.json

   tsc && node build
   

4. Create the API that will serve the data

   • In the same directory, create a file called index.ts
   • Load the targetLookup file created by the build script

    import fs from "fs";
    const {reviver} = require("../kinase-cancer-predictions/utilities");
    const targetLookup = JSON.parse(fs.readFileSync(__dirname + '/targetLookup.json', {encoding: "utf-8"}), reviver);
   

   • create the endpoint that will serve the data
     • you should make sure to call setHeaders, or do everything setHeaders does

    import {Request, Response} from "express";
    import {setHeaders} from "../../models/endpoints";
    export function associations(req: Request, res: Response): any {
        setHeaders(res);
        res.end("Hello world!"); // TODO - serve target disease associations instead
    }
    exports.associations = associations;
   

   • edit app.ts to add a route for your new endpoint

    import {associations} from "./data_sources/alliance-genome/index"
    app.get("/associations?*", (req: Request, res: Response) => associations(req, res));
   

   • visit localhost:3001/associations to make sure everything is working
   • after the call to setHeaders, fetch the appropriate dataset, given an input argument. the whole function should look like this at this point:

    export function associations(req: Request, res: Response): any {
        setHeaders(res);
        const parsedUrl = url.parse(req.url);
        const queryMap = querystring.parse(parsedUrl.query);
   
        if (queryMap.target) {
            const targetQuery = queryMap.target.toString();
            const associations = targetLookup.get(targetQuery);
            const formattedAssociations = associations; // TODO - format with JSON-LD
            res.end(JSON.stringify(formattedAssociations));
            return;
        }
        res.end("No target provided!");
    }
   

   • restart your server with tsc && node app and test in the browser again
     • Example: http://localhost:3001/associations?target=CAMK2A
     • It should show a JSON object with data from alliance genome
   • Format the JSON object for Pharos consumption, use the helper functions to help ensure the structured data is properly structured

   if (queryMap.target) {
        const targetQuery = queryMap.target.toString();
        const associations = targetLookup.get(targetQuery);
        if (associations && associations.length > 0) {
            const ps = new PredictionSet("Associated Disease", "MedicalCondition", "evidence",
                "An ECO code representing the evidence for the association", null, null);
            associations.forEach((assoc: any) => {
                const extraFields: any =
                {
                    identifier: [
                    {
                        "@type": "PropertyValue",
                        "name": "type",
                        "value": assoc.type
                    },
                    {
                        "@type": "PropertyValue",
                        "name": "reference",
                        "value": assoc.reference
                    }]
                };
                ps.addPrediction(assoc.diseaseTerm, assoc.diseaseID, assoc.evidenceCodeName, extraFields);
            });
            ps.addCitation(getMinimalCitation(35380658));
            res.end(JSON.stringify([ps.asJSON()]));
            return;
        }
    }
   

   • This code creates a PredictionSet object, where the data you are reporting is a "Associated Disease" which is a standard @MedicalCondition object (as defined by schema.org). The confidence metric will be called "evidence" with a better description of the of that confidence metric as well.
   • Later, we loop through all the associations that Alliance Genome has for this target format a couple of the fields we want to show into the "identifier" property of the @MedicalCondition object. Then we add the association data to the PredictionSet object
   • We add the citation to the PredictionSet using the PMID that Alliance Genome likes to be cited by.
   • Finally, we return a string formatted version of the PredictionSet object from our API

5. Restart your server with tsc && node app and test in the browser again Example: http://localhost:3001/associations?target=CAMK2A It should show a JSON object with data from alliance genome

6. Go back to the Pharos Toolbox https://pharos-frontend-dev.appspot.com/toolbox and enter your localhost API into the API Url field.

   • Replace the gene symbol with the wildcard {sym} so that Pharos will call your API using the appropriate gene symbol for the target details page that is being shown
   • http://localhost:3001/associations?target={sym}
   • Select a Target from the Search box at the top, and watch the magic.
   • 

7. You should see:

   • A link and blurb about the citation you provided in the PredictionSet object
   • A link to explore diseases - this navigates to a disease list page, where diseases are resolved based on the DOIDs in your list
   • A pageable table of disease associations for this target, as reported by Alliance Genome
   • The table headers correspond to the text provided to the constructor of the PredictionSet object
   • The "identifiers" given are shown as columns in the table, allowing a user to know a target "is_implicated_in" a particular disease
   • For diseases, Pharos links to each disease details page when there is one

Options

1. You can also display your data in a card view by specifying the format ("table" or "card") when you create the PredictionSet object

    const ps = new PredictionSet("Associated Disease", "MedicalCondition", "evidence",
    "An ECO code representing the evidence for the association", null, null, "card");
   

2. If you wanted to take it further, you could also build a diseaseLookup table and add functionality to return a set of Associated Targets when Pharos presents a disease details page

Caveats

1. The "Preview on Page" functionality only works with APIs deployed to the internet somewhere. See the next step for a way to do that relatively easily. The Pharos backend doens't have the ability to query your localhost. It took a bit of magic for the Toolbox page to be able to do that, that the "Preview on Page" functionality doesn't do.

Integrate your data into Pharos

The easiest way would be to do your development in a fork of this repo, and submit a pull request. We can add your API to our GCP functions to host it.

• We'll review the technical aspects of the code, and the quality and nature of the data being displayed, based on the Criteria for Predictive Models that is available on our FAQ page (https://pharos.nih.gov/faq).
• Reach out to us at [email protected] early in your development, and let us know what questions you have and if there's any functionality you'd like to see added.