| Literature DB >> 30444611 |
Floriane Larras1, Elise Billoir2, Vincent Baillard2, Aurélie Siberchicot3, Stefan Scholz1, Tesfaye Wubet4,5, Mika Tarkka5,6, Mechthild Schmitt-Jansen1, Marie-Laure Delignette-Muller3.
Abstract
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 modeling 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 modeling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput data sets 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 provides restitution for a table of results that can be directly used to perform ERA approaches.Mesh:
Year: 2018 PMID: 30444611 DOI: 10.1021/acs.est.8b04752
Source DB: PubMed Journal: Environ Sci Technol ISSN: 0013-936X Impact factor: 9.028