DRomics: A Turnkey Tool to Support the Use of the Dose-Response Framework for Omics Data in Ecological Risk Assessment
Authors/Creators
- 1. Helmholtz-Centre for Environmental Research UFZ, Department of Bioanalytical Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany
- 2. Université de Lorraine, CNRS, UMR 7360, LIEC, Laboratoire Interdisciplinaire des Environnements Continentaux, 57070 Metz, France
- 3. Université de Lyon, Université Lyon 1, CNRS, VetAgro Sup, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 69622 Villeurbanne, France
- 4. Department of Community Ecology, Helmholtz-Centre for Environmental Research – UFZ, Theodor- Lieser-Straße 4, 06120 Halle, Germany
- 5. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
Description
Omics approaches (e.g. transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modelling of concentration-dependency which may have different trends including monotonic (e.g. linear, exponential) or biphasic (e.g. U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modelling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput datasets is time-consuming and
requires effective and automated processing routines. Thus, we developed an open source tool (DRomics, available as an R-package and as a web-based service) which, after elimination of molecular responses
(e.g. gene expressions from microarrays) with no concentration-dependency and/or high variability, identifies the best model for concentration-response curve description. Subsequently, an EC (e.g. a
benchmark dose) is estimated from each curve and curves are classified based on their model parameters. This tool is especially dedicated to manage data obtained from an experimental design favoring a great
number of tested doses rather than a great number of replicates and also to handle properly monotonic and biphasic trends. The tool finally restitutes a table of results that can be directly used to perform ERA
approaches.