grounding-search

Description

The identification of sub-cellular biological entities is an important consideration in the use and creation of bioinformatics analysis tools and accessible biological research apps. When research information is uniquely and unambiguously identified, it enables data to be accurately retrieved, cross-referenced, and integrated. In practice, biological entities are “identified” when they are associated with a matching record from a knowledge base that specialises in collecting and organising information of that type (e.g. gene sequences). Our search service increases the efficiency and ease of use for identifying biological entities. This identification may be used to power research apps and tools where common entity synonyms may be provided as input.

For instance, Biofactoid uses this grounding service to allow users to simply specify their preferred synonyms to identify biological entities (e.g. proteins):

biofactoid-grounding.mp4

Citation

To cite the Pathway Commons Grounding Search Service in a paper, please cite the Journal of Open Source Software paper:

Franz et al., (2021). A flexible search system for high-accuracy identification of biological entities and molecules. Journal of Open Source Software, 6(67), 3756, https://doi.org/10.21105/joss.03756

View the paper at JOSS or view the PDF directly.

Maintenance

The Pathway Commons Grounding Search Service is an academic project built and maintained by: Bader Lab at the University of Toronto , Sander Lab at Harvard , and the Pathway and Omics Lab at the Oregon Health & Science University .

Funding

This project was funded by the US National Institutes of Health (NIH) [U41 HG006623, U41 HG003751, R01 HG009979 and P41 GM103504].

Quick start

Via Docker

Install Docker (>=20.10.0) and Docker Compose (>=1.29.0).

Clone this remote or at least the docker-compose.yml file then run:

docker-compose up

Swagger documentation can be accessed at http://localhost:3000.

NB: Server start will take some time in order for Elasticsearch to initialize and for the grounding data to be retrieved and the index restored. If it takes more than 10 minutes consider increasing the allocated memory for Docker: Preferences > Resources > Memory and remove this line in docker-compose.yml: ES_JAVA_OPTS=-Xms2g -Xmx2g

Via source

With Node.js (>=8) and Elasticsearch (>=6.6.0, <7) installed with default options, run the following in a cloned copy of the repository:

• npm install: Install npm dependencies
• npm run update: Download and index the data
• npm start: Start the server (by default on port 3000)

Documentation

Swagger documentation is available on a publicly-hosted instance of the service at https://grounding.baderlab.org. You can run queries to test the API on this instance.

Please do not use https://grounding.baderlab.org for your production apps or scripts.

Example usage

Here, we provide usage examples in common languages for the main search API. For more details, please refer to the Swagger documentation at https://grounding.baderlab.org, which is also accessible when running a local instance.

Example search in JS

const response = await fetch('http://hostname:port/search', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({ // search options here
    q: 'p53'
  })
});

const responseJSON = await response.json();

Example search in Python

import requests

url = 'http://hostname:port/search'
body = {'q': 'p53'}

response = requests.post(url, data = body)

responseJSON = response.json()

Example in shell script via curl

curl -X POST "http://hostname:port/search" -H  "accept: application/json" -H  "Content-Type: application/json" -d "{  \"q\": \"p53\" }"

Tool comparison

Here, we summarise a set of tools that overlap to some degree with the main use case of the Pathway Commons Grounding Search Service, where a user searches for a biological entity grounding by providing only a commonly-used synonym. This table was last updated on 25 October 2021 (2021-10-25).

If you have developed a new tool in this space or your tool supports new features, let us know by making a pull request, and we'll add your revision to this table.

	PC Grounding Search	GProfiler	GNormPlus (PubTator)	Gilda	BridgeDB
Allows for searching by synonym	●		●	●
Supports multiple organisms	●	●	●	●	●
Accepts organism ranking preference	●
Multiple organisms per query	●		●	Partial support (only one organism returned)
Multiple results per query	●			One per type (e.g. protein)	●
Multiple results are ranked based on relevance	●			●
Speed/Throughput	< 100 ms	< 100 ms	< 100ms	< 100 ms	< 1000 ms
Allows querying for a particular grounding by ID	●	●	●	●	●

Grounding data

grounding-search uses data files provided by three public databases:

• NCBI Gene
  • Information about genes
  • Alias: ncbi
  • Data file: gene_info.gz
• ChEBI (chebi)
  • Information about small molecules of biological interest
  • Alias: chebi
  • Data file: chebi.owl
• UniProt (uniprot)
  • Information about proteins
  • Alias: uniprot
  • Data file: uniprot_sprot.xml.gz

Build index from source database files

If you have followed the Quick Start ("Run from source"), you can download and index the data provided by the source databases ncbi, chebi and uniprot by running:

npm run update

Restore index from Elasticsearch dump files

Downloading and building the index from source ensures that the latest information is indexed. Alternatively, to quickly retrieve and recreate the index a dump of a previously indexed Elasticsearch instance has been published on Zenodo under the following DOI:

This data is published under the Creative Commons Zero v1.0 Universal license.

To restore, create a running Elasticsearch instance and run:

npm run restore

To both restore and start the grounding-search server run:

npm run boot

NB: Index dump published on Zenodo is offered for demonstration purposes only. We do not guarantee that this data will be up-to-date or that releases of grounding-search software will be compatible with any previously published version of the dump data. To ensure you are using the latest data compatible with grounding-search, follow instructions in "Build the index database from source database files".

Issues & feedback

To let us know about an issue in the software or to provide feedback, please file an issue on GitHub.

Contributing

To make a contribution to this project, please start by please filing an issue on GitHub that describes your proposal. Once your proposal is ready, you can make a pull request.

Configuration

The following environment variables can be used to configure the server:

• NODE_ENV : the environment mode, either production or development (default)
• LOG_LEVEL : the level for the log file (info, warn, error)
• PORT : the port on which the server runs (default 3000)
• ELASTICSEARCH_HOST : the host:port that points to elasticsearch
• MAX_SEARCH_ES : the maximum number of results to return from elasticsearch
• MAX_SEARCH_WS : the maximum number of results to return in json from the webservice
• CHUNK_SIZE : how many grounding entries make up a chunk that gets bulk inserted into elasticsearch
• MAX_SIMULT_CHUNKS : maximum number of chunks to insert simulteneously into elasticsearch
• INPUT_PATH : the path to the input folder where data files are located
• INDEX : the elasticsearch index name to store data from all data sources
• UNIPROT_FILE_NAME : name of the file where uniprot data will be read from
• UNIPROT_URL : url to download uniprot file from
• CHEBI_FILE_NAME : name of the file where chebi data will be read from
• CHEBI_URL : url to download chebi file from
• NCBI_FILE_NAME : name of the file where ncbi data will be read from
• NCBI_URL : url to download ncbi file from
• NCBI_EUTILS_BASE_URL : url for NCBI EUTILS
• NCBI_EUTILS_API_KEY : NCBI EUTILS API key
• FAMPLEX_URL: url to download FamPlex remote from
• FAMPLEX_FILE_NAME: name of the file where FamPlex data will be read from
• FAMPLEX_TYPE_FILTER: entity type to include ('protein', 'complex', 'all' [default])
• ESDUMP_LOCATION : The location (URL, file path) of elasticdump files (note: terminate with '/')
• ZENODO_API_URL: base url for Zenodo
• ZENODO_ACCESS_TOKEN: access token for Zenodo REST API (Scope: deposit:actions, deposit:write)
• ZENODO_BUCKET_ID: id for Zenodo deposition 'bucket' (Files API)
• ZENODO_DEPOSITION_ID: id for Zenodo deposition (for a published dataset)

Run targets

• npm start : start the server
• npm stop : stop the server
• npm run watch : watch mode (debug mode enabled, autoreload)
• npm run refresh : run clear, update, then start
• npm test : run tests for read only methods (e.g. search and get) assuming that data is already existing
• npm test:sample : run tests with sample data
• npm run test:quality : run the search quality tests (expects full db)
• npm run test:quality:csv : run the search quality tests and output a csv file
• npm run lint : lint the project
• npm run benchmark : run all benchmarking
• npm run benchmark:source : run benchmarking for source (i.e. ncbi, chebi)
• npm run clear : clear all data
• npm run clear:source : clear data for source (i.e. ncbi, chebi)
• npm run update : update all data (download then index)
• npm run update:source : update data for source (i.e. ncbi, chebi) in elasticsearch
• npm run download : download all data
• npm run download:source download data for source (i.e. ncbi, chebi)
• npm run index : index all data
• npm run index:source : index data for source (i.e. ncbi, chebi) in elasticsearch
• npm run test:inputgen : generate input test file for each source (i.e. uniprot, ...)
• npm run test:inputgen : generate input test file for source (i.e. uniprot, ...)
• npm run dump : dump the information for INDEX to ESDUMP_LOCATION
• npm run restore : restore the information for INDEX from ESDUMP_LOCATION
• npm run boot : run clear, restore then start; exit on errors

Using Zenodo to store index dumps

Zenodo lets you you to store and retrieve digital artefacts related to a scientific project or publication. Here, we use Zenodo to store Elasticsearch index dump data used to quickly recreate the index used by grounding-search.

Create and publish a new record deposition

Briefly, using their RESTful web service API, you can create a 'Deposition' for a record that has a 'bucket' referenced by a ZENODO_BUCKET_ID to which you can upload and download 'files' (i.e. <ZENODO_API_URL>api/files/<ZENODO_BUCKET_ID>/<filename>; list them with https://zenodo.org/api/deposit/depositions/<deposition id>/files). In particular, there are three files required to recreate an index, corresponding to the elasticsearch types: data; mapping and analyzer.

To setup follow these steps:

1. Get a ZENODO_ACCESS_TOKEN by creating a 'Personal access token' (see docs for details). Be sure to add the deposit:actions and deposit:write scopes.
2. Create a recrod 'Deposition' by POSTing to https://zenodo.org/api/deposit/depositions with at least the following information, keeping in mind to set the header Authorization = Bearer <ZENODO_ACCESS_TOKEN>:

{
	"metadata": {
		"title": "Elasticsearch data for biofactoid.org grounding-search service",
		"upload_type": "dataset",
		"description": "This deposition contains files with data describing an Elasticsearch index (https://github.com/PathwayCommons/grounding-search). The files were generated from the elasticdump npm package (https://www.npmjs.com/package/elasticdump). The data are the neccessary and sufficient information to populate an Elasticsearch index.",
		"creators": [
			{
				"name": "Biofactoid",
				"affiliation": "biofactoid.org"
			}
		],
		"access_right": "open",
		"license": "cc-zero"
	}
}

3. The POST response should have a 'bucket' (e.g. "bucket": "https://zenodo.org/api/files/<uuid>" ) within the links object. The variable ZENODO_BUCKET_ID is the value <uuid> in the example URL.
4. Publish. You'll want to dump the index and upload to Zenodo (npm run dump). You can publish this from the API by POSTing to https://zenodo.org/api/deposit/depositions/<deposition id>/actions/publish. Alternatively, log in to the Zenodo web page and click 'Publish' to make the deposition public.

Once published, a deposition cannot be updated or altered. However, you can create a new version of a record (below).

Create and publish a new version of a record

In this case, you already have a record which points to a published deposition (i.e. elasticsearch index files) and wish to create a new version for that record. Here, you'll create a new deposition under the same record:

1. Make a POST request to https://zenodo.org/api/deposit/depositions/<deposition id>/actions/newversion to create a new version. Alternatively, visit https://zenodo.org/record/<deposition id> where deposition id is that of the latest published version (default).
2. Fetch https://zenodo.org/api/deposit/depositions?all_versions to list all your depositions and identify the new deposition bucket id.
3. Proceed to upload (i.e. dump) your new files as described in "Create a new deposition", Step 3.

• Notes:
  • New version's files must differ from all previous versions
  • See https://help.zenodo.org/#versioning and https://developers.zenodo.org/#new-version for more info

Testing

All files /test will be run by Mocha. You can npm test to run all tests, or you can run npm test -- -g specific-test-name to run specific tests.

Chai is included to make the tests easier to read and write.

Publishing a release

1. Make sure the tests are passing: npm test
2. Make sure the linting is passing: npm run lint
3. Bump the version number with npm version, in accordance with semver. The version command in npm updates both package.json and git tags, but note that it uses a v prefix on the tags (e.g. v1.2.3).
4. For a bug fix / patch release, run npm version patch.
5. For a new feature release, run npm version minor.
6. For a breaking API change, run npm version major.
7. For a specific version number (e.g. 1.2.3), run npm version 1.2.3.
8. Push the release: git push && git push --tags
9. Publish a GitHub release so that Zenodo creates a DOI for this version.