Welcome to the DRomics Shiny Application


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) rather than a great number of replicates (no need of three replicates).
After a first optional step which consists to import, check and if needed normalize/transform 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 benchmark dose or concentration from each fitted curve (step 4).
In the available version, DRomics supports single-channel microarray data (in log2 scale), RNAseq data (in raw counts) or metabolomics data (in log scale). In order to link responses across biological levels based on a common method, DRomics also handles apical data as long as they are continuous and follow a Gaussian distribution for each dose or concentration, with a common standard error. DRomics should not be used on other types of data.

DRomics Shiny App runs on the shiny server of the LBBE (see here the DRomics tutorial ), with the develoment version of the DRomics package (available on Github ).

DRomics is also an R package, available on CRAN and on this web page .


If you use Dromics Shiny App, you should cite:

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. https://doi.org/10.1021/acs.est.8b04752


You can find this article at: https://hal.archives-ouvertes.fr/hal-02309919


You can also look at the following citation for a complete example of use:

Larras F, Billoir E, Scholz S, Tarkka M, Wubet T, Delignette-Muller ML, Schmitt-Jansen M (2020).
A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus.
Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2020.122727


Authors & Contacts

Elise Billoir - elise.billoir@univ-lorraine.fr - Laboratoire Interdisciplinaire des Environnements Continentaux - Université de Lorraine - Metz - France

Marie-Laure Delignette-Muller - marielaure.delignettemuller@vetagro-sup.fr - Laboratoire de Biométrie et Biologie Evolutive - VetAgro Sup - Lyon - France

Floriane Larras - floriane.larras@ufz.de - Department of Bioanalytical Ecotoxicology - Helmholtz Center for Environmental Research GmbH - Leipzig - Germany

Mechthild Schmitt-Jansen - mechthild.schmitt@ufz.de - Department of Bioanalytical Ecotoxicology - Helmholtz Center for Environmental Research GmbH - Leipzig - Germany


Technical maintainer

Aurélie Siberchicot - aurelie.siberchicot@univ-lyon1.fr - Laboratoire de Biométrie et Biologie Evolutive - Université Lyon 1 - Lyon - France

Issues can be reported on https://github.com/aursiber/DRomics/issues .


Grant Agreement number: 705149 - MicroERA - H2020-MSCA-IF-2015

Horizon 2020




IMPORT, CHECK AND PRETREATMENT OF OMICS DATA



See here information about the format required
See here an example file
See here information about the normalization methods
See here information about the format required
See here an example file
Be aware that counts are automatically rounded to ensure compatibility of counts from Kallisto or Salmon with the tool.
See here information about the transformation methods
See here information about the format required
See here an example file
We recommend you to check that your metabolomics data were correctly pretreated before importation. In particular data (metabolomic signal) should have been log-transformed, without replacing 0 values by NA values (consider using the half minimum method instead for example).
See here more information about metabolomics data pretreatment
See here information about the format required
See here an example file
We recommend you to check that your anchoring data are continuous and expressed in a scale that enables the use of a Gaussian error model (a transformation of data may be needed for some endpoints). If this assumption is not respected, results of selection and further steps may be inaccurate.

                  
Loading...


SELECTION OF SIGNIFICANTLY RESPONSIVE ITEMS


See here information about the selection methods

Loading...

                  

DOSE RESPONSE MODELLING FOR RESPONSIVE ITEMS


Click this button each time you update a setting in previous steps:
See here information about the dose reponse modelling procedure
These ongoing calculations can take from minutes to about an hour. Your patience should be proportional to the size of your data and the chosen FDR.

                  

Loading...



COMPUTATION OF BENCHMARK DOSES FOR RESPONSIVE ITEMS


See here information about the BMD-zSD and the BMD-xfold

Download results
See here information about the provided results

                







See here information about typologies

Download figure