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README.md

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Deployment instructions

Prerequisites

You should have installed:

Installation

All of the commands should be executed from the deploy directory.

cd deploy

Light up the database and the Beacon

Up the containers

docker-compose up -d --build

With mongo-express we can see the contents of the database at http://localhost:8081.

Load the data

To load the database we execute the following commands:

docker cp /path/to/analyses.json deploy_db_1:tmp/analyses.json
docker cp /path/to/biosamples.json deploy_db_1:tmp/biosamples.json
docker cp /path/to/cohorts.json deploy_db_1:tmp/cohorts.json
docker cp /path/to/datasets.json deploy_db_1:tmp/datasets.json
docker cp /path/to/genomicVariationsVcf.json deploy_db_1:tmp/genomicVariations.json
docker cp /path/to/individuals.json deploy_db_1:tmp/individuals.json
docker cp /path/to/runs.json deploy_db_1:tmp/runs.json
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/datasets.json --collection datasets
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/analyses.json --collection analyses
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/biosamples.json --collection biosamples
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/cohorts.json --collection cohorts
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/genomicVariations.json --collection genomicVariations
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/individuals.json --collection individuals
docker exec deploy_db_1 mongoimport --jsonArray --uri "mongodb://root:[email protected]:27017/beacon?authSource=admin" --file /tmp/runs.json --collection runs

This loads the JSON files inside of the data folder into the MongoDB database container.

Create the indexes

You can create the necessary indexes running the following Python script:

docker exec beacon python beacon/reindex.py

Fetch the ontologies and extract the filtering terms

This step consists of analyzing all the collections of the Mongo database for first extracting the ontology OBO files and then filling the filtering terms endpoint with the information of the data loaded in the database.∫

You can automatically fetch the ontologies and extract the filtering terms running the following script:

docker exec beacon python beacon/db/extract_filtering_terms.py

Get descendant and semantic similarity terms

If you have the ontologies loaded and the filtering terms extracted, you can automatically get their descendant and semantic similarity terms running the following script:

docker exec beacon python beacon/db/get_descendants.py

Check the logs

Check the logs until the beacon is ready to be queried:

docker-compose logs -f beacon

Usage

You can query the beacon using GET or POST. Below, you can find some examples of usage:

For simplicity (and readability), we will be using HTTPie.

Using GET

Querying this endpoit it should return the 13 variants of the beacon (paginated):

http GET http://localhost:5050/api/g_variants

You can also add request parameters to the query, like so:

http GET http://localhost:5050/api/individuals?filters=NCIT:C16576,NCIT:C42331

Using POST

You can use POST to make the previous query. With a request.json file like this one:

{
    "meta": {
        "apiVersion": "2.0"
    },
    "query": {
        "requestParameters": {
    "alternateBases": "G" ,
    "referenceBases": "A" ,
"start": [ 16050074 ],
            "end": [ 16050568 ],
	    "variantType": "SNP"
        },
        "filters": [],
        "includeResultsetResponses": "HIT",
        "pagination": {
            "skip": 0,
            "limit": 10
        },
        "testMode": false,
        "requestedGranularity": "record"
    }
}

You can execute:

curl \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "meta": {
        "apiVersion": "2.0"
    },
    "query": {
        "requestParameters": {
    "alternateBases": "G" ,
    "referenceBases": "A" ,
"start": [ 16050074 ],
            "end": [ 16050568 ],
	    "variantType": "SNP"
        },
        "filters": [],
        "includeResultsetResponses": "HIT",
        "pagination": {
            "skip": 0,
            "limit": 10
        },
        "testMode": false,
        "requestedGranularity": "record"
    }
}' \
  http://localhost:5050/api/g_variants

But you can also use complex filters:

{
    "meta": {
        "apiVersion": "2.0"
    },
    "query": {
        "filters": [
            {
                "id": "UBERON:0001256",
                "scope": "biosamples",
                "includeDescendantTerms": false
            }
        ],
        "includeResultsetResponses": "HIT",
        "pagination": {
            "skip": 0,
            "limit": 10
        },
        "testMode": false,
        "requestedGranularity": "count"
    }
}

You can execute:

http POST http://localhost:5050/api/biosamples --json < request.json

And it will use the ontology filter to filter the results.