Transcriptomics data from human, mouse, rat in vitro liver models

This repository contains transcriptomics data from human, mouse and rat in vitro liver cell models. Only meta-data information file, normalized data and average of normalized array data of all replicates (mostly 3 replicates) are available in the sub-folders.

For end-users
For developers
Database / data source
Omics database, Toxicology, chemical properties and bioassay databases
Applicability domain:
Toxicology, Predictive toxicology
Chemical properties, Risk assessment, Predictive modelling
Biological area:
Transcriptomics, Genotoxicity, Carcinogenicity
Targeted users:
Students, Researchers
Relevant OpenRiskNet case studies:
  • AOPLink - Identification and Linking of Data related to AOPWiki
  • DataCure - Data curation and creation of pre-reasoned datasets and searching
  • ModelRX - Modelling for Prediction or Read Across
  • RevK - Reverse dosimetry and PBPK prediction
  • SysGroup - A systems biology approach for grouping compounds
  • TGX - Toxicogenomics-based prediction and mechanism identification

Provided by:
Department of Toxicogenomics, Maastricht University
Login required:
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, and TeSS (ELIXIR).
Provides legal and ethical statements on how the service can be used.

Resources & Training

Case Study description - Toxicogenomics-based prediction and mechanism identification [TGX]
Danyel Jennen and Jumamurat Bayjanov (UM), Evan Floden (CRG)
17 Oct 2019
In this case study a transcriptomics-based hazard prediction model for identification of specific molecular initiating events (MIE) will be applied based on (A) top-down and (B) bottom-up approaches. The MIEs can include, but are not limited to: (1) Genotoxicity (p53 activation), (2) Oxidative stress (Nrf2 activation), (3) Endoplasmic Reticulum Stress (unfolded protein response), (4) Dioxin-like activity (AhR receptor activation), (5) HIF1 alpha activation and (6) Nuclear receptor activation (e.g. for endocrine disruption).
Additional materials:

Target audience: Researchers, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: CRG, UM
Big Data in Toxicogenomics: Towards FAIR predictions
Danyel Jennen
26 Jul 2018
Additional materials:

Target audience: Risk assessors, Researchers, Bioinformaticians
Organisations involved: UM