BridgeDb identifier mapping service

BridgeDb is a platform for database identifier mapping, both simple identifiers (e.g. CHEBI:1234) and universal resource identifiers (URIs, e.g. http://identifiers.org/chebi/CHEBI:1234). It is the workhorse of data integration and supports the essential FAIR aspect of interoperability, with recent efforts adding detailed provenance and meaning to mappings (“scientific lenses”). BridgeDb provides the glue between bioinformatics processing pipeline blocks, and has an OpenAPI-based interface. This service provides identifier mapping for metabolites and genes/proteins from a large variety of species.

For developers
For end-users
Type:
Database / data source, Service
API Type:
REST under OpenAPI2 specification
Categories:
Ontology services, API Definitions for OpenRiskNet applications and data
Applicability domain:
Bioinformatics
Topic:
Identifier mapping
Targeted industry:
Chemicals, Drugs, Cosmetics, Food, Other consumer products
Targeted users:
Software Developers, Risk assessors, Researchers, Students, Data managers
Relevant OpenRiskNet case study:
AOPLink - Identification and Linking of Data related to AOPWiki

Provided by:
Maastricht University
Licence:
Apache 2.0
Login required:
No
Implementation status:
Available as web service, API documentation available (Swagger-OpenAPI v2)
Integration status:
Integrated application
Service integration operations completed:
Utilises the OpenRiskNet APIs to ensure that each service is accessible to our proposed interoperability layer.
Is annotated according to the semantic interoperability layer concept using defined ontologies.
Is containerised for easy deployment in virtual environments of OpenRiskNet instances.
Has documented scientific and technical background.
Is deployed into the OpenRiskNet reference environment.
Is listed in the OpenRiskNet discovery services.
Is listed in other central repositories like eInfraCentral, bio.tools and TeSS (ELIXIR).
Provides legal and ethical statements on how the service can be used.

Resources & Training

Report
Identification and Linking of Data related to AOPs of AOP-Wiki [AOPLink]
Marvin Martens and Egon Willighagen (Maastricht University, Department of Bioinformatics - BiGCaT)
7 Oct 2019
Abstract:
The Adverse Outcome Pathway (AOP) concept has been introduced to support risk assessment (Ankley et al., 2010). An AOP is initiated upon exposure to a stressor that causes a Molecular Initiating Event (MIE), followed by a series of Key Events (KEs) on increasing levels of biological organization. Eventually, the chain of KEs ends with the Adverse Outcome (AO), which describes the phenotypic outcome, disease, or the effect on the population. In general, an AOP captures mechanistic knowledge of a sequence of toxicological responses after exposure to a stressor. While starting with molecular information, for example, the initial interaction of a chemical with a cell, the AOPs contain information of downstream responses of the tissue, organ, individual and population. Currently, AOPs are stored in the AOP-Wiki, a collaborative platform to exchange mechanistic toxicological knowledge as a part of the AOP-KB, an initiative by the OECD. Normally, AOP development starts with a thorough literature search for existing knowledge, describing the sequence of KEs that form the AOP. However, the use of AOPs for regulatory purposes also requires detailed validation and linking to existing knowledge (Knapen et al., 2015; Burgdorf et al., 2017). Part of the development of AOPs is the search for data that supports the occurrence and biological plausibility of KEs and their relationships (KERs). This type of data can be found in literature, and increasingly in public databases. The main goal of this case study is to establish the links between AOPs of the AOP-Wiki and experimental data to support a particular AOP. This will allow finding AOPs related to experimental data, and finding data related to a particular AOP.
Additional materials:
Report

Publisher: OpenRiskNet
Target audience: Risk assessors, Researchers, Students, Nanosafety community, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: UM
Report
Webinar recording
Connecting Adverse Outcome Pathways, knowledge and data with AOPLink workflows
Marvin Martens (Department of Bioinformatics, Maastricht University, The Netherlands)
16 Jul 2019

Target audience: Risk assessors, Researchers, Students, Nanosafety community, Regulators, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: UM
Webinar recording
Presentation
Connecting Adverse Outcome Pathways, knowledge and data with AOPLink workflows
Marvin Martens (Department of Bioinformatics, Maastricht University, The Netherlands)
15 Jul 2019
Additional materials:
Slides

Target audience: Risk assessors, Researchers, Students, Nanosafety community, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: UM
Presentation
Presentation
AOPLink - Linking experimental data to Adverse Outcome Pathways
Marvin Martens, Egon Willighagen, Chris Evelo (Department of Bioinformatics, Maastricht University, The Netherlands)
27 Mar 2019
Additional materials:
Slides

Publisher: OpenRiskNet
Target audience: Risk assessors, Researchers, Students, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: UM
Presentation
Tutorial
Workflow: Access TG-GATEs data for seleted compounds, select differentially expressed genes and identifier relevant pathways
Thomas Exner
13 Sep 2018
Abstract:
Example workflow based on OpenRiskNet tools - Pathway identification workflow related to DataCure and AOPlink case studies. This notebook downloads TG-Gates data of 4 compounds and selects genes overexpressed in all sample. The Affymetrix probe sets are then translated into Ensembl gene identifiers using the BridgeDB service and pathways associated with the genes are identified using the WikiPathways service.

Publisher: OpenRiskNet
Target audience: Risk assessors, Researchers, Data modellers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC
Tutorial