DRomics is a freely available 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 replicates to use
After a first step which consists in importing, checking and if needed normalizing/transforming the data (step 1), the aim of the proposed workflow is to select monotonic and/or biphasic significantly responsive items (e.g. probes, contigs, 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). Those steps can be performed in R using
DRomics functions, or using the shiny application named
In the available version,
DRomics supports single-channel microarray data (in log2 scale), RNAseq data (in raw counts) and other continuous omics data such as metabolomics or proteomics (in log scale). In order to link responses across biological levels based on a common method,
DRomics also handles continuous apical data as long as they meet the use conditions of least squares regression (homoscedastic Gaussian regression).
As built in the environmental risk assessment context where omics data are more often collected on non-sequenced species or species communities,
DRomics does not provide an annotation pipeline. The annotation of items selected by
DRomics may be complex in this context, and must be done outside
DRomics using databases such as KEGG or Gene Ontology.
DRomics functions can then be used to help the interpretation of the workflow results in view of the biological annotation. It enables a multi-omics approach, with the comparison of the responses at the different levels of organization (in view of a common biological annotation). It can also be used to compare the responses at one organization level, but measured under different experimental conditions (e.g. different time points). This interpretation can be performed in R using
DRomics functions, or using a second shiny application
All informations about DRomics can also be found at https://lbbe.univ-lyon1.fr/fr/dromics.
Keywords : dose response modelling / benchmark dose (BMD) / environmental risk assessment / transcriptomics / proteomics / metabolomics / toxicogenomics / multi-omics
DESeq2 packages from Bioconductor must be installed for the use of
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("limma", "DESeq2"))
The stable version of
DRomics can be installed from CRAN using:
The development version of
DRomics can be installed from GitHub (
if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes") remotes::install_github("aursiber/DRomics")
Finally load the package in your current R session with the following R command:
A vignette is attached to the
This vignette is intended to help users to start using the
DRomics package. It is complementary to the reference manual where you can find more details on each function of the package. The first part of this vignette (Main workflow, steps 1 to 4) could also help users of the first shiny application
DRomics-shiny. The second part (Help for biological interpretation of
DRomics outputs) could also help users of the second shiny application
This vignette can be reached by:
Note that, by default, the vignette is not installed when the package is installed through GitHub. The following command (rather long to execute because of the large size of the vignette) will allow you to access the vignette of the development version of the package you installed from GitHub:
remotes::install_github("aursiber/DRomics", build_vignettes = TRUE)
A cheat sheet that sum up the DRomics workflow is also available here.
The two shiny apps (
DRomicsInterpreter-shiny) that work with DRomics are available :
install.packages(c("shiny", "shinyBS", "shinycssloaders", "shinyjs", "shinyWidgets", "sortable"))
shiny::runApp(system.file("DRomics-shiny", package = "DRomics"))
shiny::runApp(system.file("DRomicsInterpreter-shiny", package = "DRomics"))
These shiny apps are runing with the development version of DRomics.
If you have any need that is not yet covered, any feedback on the package / Shiny app, or any training needs, feel free to email us at firstname.lastname@example.org .
Issues can be reported on https://github.com/aursiber/DRomics/issues .
If you use Dromics, 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://pubs.acs.org/doi/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.
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