ToxCast/Tox21 V3.2
ToxCast and Tox21 datasets (raw and summary) extracted from the MySQL database provided by US EPA
The dataset as provided by US EPA were transformed and are now available in the EdelweissData system for easy access via APIs.
The most current version is 3.2. Data of version 3.1 is also available.
ToxCast: Data for approximately 1,800 chemicals from a broad range of sources including industrial and consumer products, food additives, and potentially green chemicals that could be safer alternatives to existing chemicals is provided. These chemicals were screened in more than 700 high-throughput assay endpoints that cover a range of high-level cell responses.
Tox21: Using a high-throughput robotic screening system housed at NCATS, researchers are testing 10,000 environmental chemicals (called the Tox21 10K library) for their potential to disrupt biological pathways that may result in toxicity. Screening results help the researchers prioritize chemicals for for further in-depth investigation.
For end-users
For developers
Type:
Database / data source
API Type:
OpenAPI, REST, REST under OAS3 specification
Categories:
Toxicology, chemical properties and bioassay databases
Applicability domain:
Computational modelling, Toxicology, Predictive toxicology
Topic:
Bioassay, Risk assessment, Information extraction
Biological area:
NOAEL/LOAEL
Targeted industry:
Chemicals, Drugs, Cosmetics, Food, Other consumer products
Targeted users:
Risk assessors, Researchers, Students, Software Developers, Regulators
Relevant OpenRiskNet case studies:
Provided by:
Edelweiss Connect GmbH
Contact:
Licence:
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Login required:
No
Implementation status:
Graphical user interface available, OAS v3, Available as web service, Application programming interface available
Technology readiness level:
TRL 8 – system complete and qualified
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
Case Study report - Data curation and creation of pre-reasoned datasets and searching [DataCure]
11 Dec 2019
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Abstract:
DataCure establishes a process for data curation and annotation that makes use of APIs (eliminating the need for manual file sharing) and semantic annotations for a more systematic and reproducible data curation workflow. In this case study, users are provided with capabilities to allow access to different OpenRiskNet data sources and target specific entries in an automated fashion for the purpose of identifying data and metadata associated with a chemical in general to identify possible areas of concern or for a specific endpoint of interest (Figure 1B). The datasets can be curated using OpenRiskNet workflows developed for this case study and, in this way, cleansed e.g. for their use in model development (Figure 1A). Text mining facilities and workflows are also included for the purposes of data searching, extraction and annotation (Figure 1C). A first step in this process was to define APIs and provide the semantic annotation for selected databases (e.g. FDA datasets, ToxCast/Tox21 and ChEMBL). During the preparation for these use cases, it became clear that the existing ontologies do not cover all requirements of the semantic interoperability layer. Nevertheless, the design of the annotation process as an online or an offline/preprocessing step forms an ancillary part of this case study even though the ontology development and improvement cannot be fully covered by OpenRiskNet and is instead organized as a collaborative activity of the complete chemical and nano risk assessment community.
DataCure establishes a process for data curation and annotation that makes use of APIs (eliminating the need for manual file sharing) and semantic annotations for a more systematic and reproducible data curation workflow. In this case study, users are provided with capabilities to allow access to different OpenRiskNet data sources and target specific entries in an automated fashion for the purpose of identifying data and metadata associated with a chemical in general to identify possible areas of concern or for a specific endpoint of interest (Figure 1B). The datasets can be curated using OpenRiskNet workflows developed for this case study and, in this way, cleansed e.g. for their use in model development (Figure 1A). Text mining facilities and workflows are also included for the purposes of data searching, extraction and annotation (Figure 1C). A first step in this process was to define APIs and provide the semantic annotation for selected databases (e.g. FDA datasets, ToxCast/Tox21 and ChEMBL). During the preparation for these use cases, it became clear that the existing ontologies do not cover all requirements of the semantic interoperability layer. Nevertheless, the design of the annotation process as an online or an offline/preprocessing step forms an ancillary part of this case study even though the ontology development and improvement cannot be fully covered by OpenRiskNet and is instead organized as a collaborative activity of the complete chemical and nano risk assessment community.
Additional materials:
Report
Report
Target audience: Risk assessors, Researchers, Data managers, Data owners, OpenRiskNet stakeholders, Data modellers, Bioinformaticians, Data providers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, UM, NTUA, Fraunhofer, IM