Development and Implementation of a Sustainable Modelling Platform for NanoInformatics
NanoInformaTIX develops a web-based Sustainable Nanoinformatics Framework (SNF) platform for risk management of engineered nanomaterials (ENM) in industrial manufacturing. The tool will be based on the significant amounts of data on physico-chemical and toxicological and ecotoxicological properties of ENM generated over the last decades, as well as new data coming from research.
The final aim is to provide efficient user-friendly interfaces to enhance accessibility and usability of the nanoinformatics models to industry, regulators, and civil society, thus supporting sustainable manufacturing of ENM-based products.
The Project features 36 partners across 22 countries.
For detailed information on the whole EU-funded project, please have a look at the projects website at https://www.nanoinformatix.eu/.
- Material Modelling
- Fate-Exposure Modelling
- Dose-Response Modelling
- Integration and Linking of Models
- Model Validation
UFZ is the lead beneficiary in Task 5.3 (Systems Biology Modelling) which is a part of the Workpackage 3 that generally aims at Dose-Response Modelling. Furthermore, UFZ also participates in Task 5.4 (Grouping Strategies) of Workpackage 5 and Workpackage 6 on Model improvement, validation, and integration.
Systems Biology Modelling - UFZ Contribution
Although it is widely accepted that multi-omics is needed in systems toxicology, there is a great need for data integration strategies of “Big Data” focussing on yielding biological meaningful information. In this task we will evaluate several existing approaches for the integrated analysis of the multi-omics data and will additionally develop multi-omics pathway enrichment tools to foster the applicablity of omics techniques within regulatory toxicology and regulatory applications.
The destillation of biological meaningful information from multi-omics data sets will furthermore also contribute in identifying molecular mechanisms that underlie the MIE and mode of action of nanomaterials. By going one step further and analysing also the pathway interactions and interdependences that
regulate the molecular processes behind MIEs and KEs, this task will result in a deeper understanding and hence, a mechanistic based interpretation and prediction of effects of ENM. The defined MIEs and KEs will be keystones for building an AOP of ENM, as it is required for knowledge based risk assessment.
- Canzler, S., Hackermüller, J., (2020):
multiGSEA: a GSEA-based pathway enrichment analysis for mult-omics data
BMC Bioinformatics 21, art. 561
Sebastian Canzler, Jörg Hackermüller
Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well.
In recent years the call for a combined analysis of multiple omics layer became prominent, giving rise to a few multi-omics enrichment tools. Each of which has its own drawbacks and restrictions regarding its universal application.
Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layer. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs.
|Bioconductor devel package||https://bioconductor.org/packages/devel/bioc/html/multiGSEA.html|
|Citation||Sebastian Canzler, Jörg Hackermüller. multiGSEA: A GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics 21, 561 (2020). https://doi.org/10.1186/s12859-020-03910-x|