Case Study
SysGroup – A systems biology approach for grouping compounds

Summary

This case study will use the approach of the diXa / DECO2 (Cefic-LRI AIMT4) projects to reproduce and extend the results obtained on the identification of hepatotoxicant groups based on similarity in mechanisms of action (omics-based) and chemical structure using services from OpenRiskNet.

Objectives

The objective of this case study is to implement an integrated analysis using chemoinformatics and omics data for improved grouping of compounds with similar toxicity and/or mode of action.

Risk assessment framework

SysGroup covers the identification of use scenario / chemical of concern / collection of existing information (Tier 0 in the selected framework) and its steps related to:

  • Identification of molecular structure;
  • Collection of support data;
  • Identification of analogues / suitability assessment and existing data.

Use Cases Associated

This case study is associated with UC1 - Merge existing data by a common structure identifier and includes the following steps:

  1. Chemical similarity calculated by 2D or 3D Tanimoto coefficient
  2. Protein target prediction
  3. Interface to diXa for obtaining gene expression data
  4. Integration of the multiple data sources and grouping by iClusterPlus

Databases and tools

PubChem for Tanimoto scores, ChEMBL or PIDGIN for target predictions, (pre)processing tools for gene expression data (e.g. microarray data) and iClusterPlus for the integration of the multiple types of date.

Service integration

Integration with other case studies is needed. SysGroup acquires information from the DataCure case study and can feed into AOPLink and ModelRX.

Currently available services:

  • Interactive computing and workflows sharing
    Service type: Workflow, Visualisation tool, Helper tool, Software, Analysis tool, Processing tool
  • Computation research made simple and reproducible
    Service type: Workflow, Database / data source, Service

Related resources

Poster
OpenRiskNet Part II: Predictive Toxicology based on Adverse Outcome Pathways and Biological Pathway Analysis
Marvin Martens, Thomas Exner, Nofisat Oki, Danyel Jennen, Jumamurat Bayjanov, Chris Evelo, Tim Dudgeon, Egon Willighagen
28 Aug 2019
Abstract:
The OpenRiskNet project (https://openrisknet.org/) is funded by the H2020-EINFRA-22-2016 Programme. Here we present how the concept of Adverse Outcome Pathways (AOPs), which captures mechanistic knowledge from a chemical exposure causing a Molecular Initiating Event (MIE), through Key Events (KEs) towards an Adverse Outcome (AO), can be extended with additional knowledge by using tools and data available through the OpenRiskNet e-Infrastructure. This poster describes how the case study of AOPLink, together with DataCure, TGX, and SysGroup, can utilize the AOP framework for knowledge and data integration to support risk assessments. AOPLink involves the integration of knowledge captured in AOPs with additional data sources and experimental data from DataCure. TGX feeds this integration with prediction models of the MIE of such AOPs using either gene expression data or knowledge about stress response pathways. This is complemented by SysGroup, which is about the grouping of chemical compounds based on structural similarity and mode of action based on omics data. Therefore, the combination of these case studies extends the AOP knowledge and allows biological pathway analysis in the context of AOPs, by combining experimental data and the molecular knowledge that is captured in KEs of AOPs.

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: EwC, UM, IM
Poster