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DRomics is a freely available on-line tool for dose-response (or concentration-response) characterization from omics data. It is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations, at least 6, and the more the better) rather than a great number of replicates (no need of three replicates). After a first optional step which consists to normalize the data (step 1), the aim of the proposed workflow is to select monotonic and/or biphasic significantly responsive items (e.g. probes, metabolites) (step 2), to choose the best-fit model among a predefined family of monotonic and biphasic models to describe the response of each selected item (step 3), and to derive a sensitivity value (a benchmark dose or concentration) and a typology of response from each fitted curve.
In the available version data are supposed to be single-channel microarray data transformed in log2, or another type of data that can be directly fitted by least-square regression without any normalization step. In the future this tool will also be able to process RNA-seq data.

Sources & Documentation

1- DRomics is an R package available on CRAN at :

Install the DRomics package with the following R command :

The most recent version in development of the DRomics package can be downloaded here :

GZ - 1.3 Mo

and can be installed with the following R command :
install.packages("DRomics", repos = NULL, type = "source")

Finally load the package in your current R session with the following R command :

2- The shiny app DRomics-shiny can be run on a R session, doing :
shiny::runApp(system.file("DRomics-shiny", package = "DRomics"))

or online at :

Authors & Contacts

Elise Billoir :
Marie-Laure Delignette-Muller :
Floriane Larras :
Mechthild Schmitt-Jansen :

About technical issues, you can contact Aurélie Siberchicot :


Larras F, Billoir E, Baillard V, Siberchicot A, Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M and Delignette-Muller ML (2018). DRomics : a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment. Environmental Science & Technology.