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DRomics: A Turnkey Tool to Support the Use of the Dose-Response Framework for Omics Data in Ecological Risk Assessment.

Floriane LarrasElise BilloirVincent BaillardAurélie SiberchicotStefan ScholzTesfaye WubetMika TarkkaMechthild Schmitt-JansenMarie Laure Delignette-Muller
Published in: Environmental science & technology (2018)
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.
Keyphrases
  • risk assessment
  • single cell
  • high throughput
  • electronic health record
  • human health
  • big data
  • genome wide
  • healthcare
  • machine learning
  • heavy metals
  • climate change
  • data analysis
  • deep learning