DoRothEA is a gene regulatory network (GRN) containing signed transcription factor (TF) - target gene interactions. DoRothEA regulons, the collection of a TF and its transcriptional targets, were curated and collected from different types of evidence for both human and mouse. A confidence level was assigned to each TF-target interaction based on the number of supporting evidence.
These regulons, coupled with any statistical method, can be used to infer TF activities from bulk or single-cell transcriptomics.
DoRothEA is available in Bioconductor. In addition, one can install the development version from the Github repository:
Since the original release, we have implemented some extensions in DoRothEA:
Extension to mouse: Originally DoRothEA was developed for the application to human data. In a benchmark study we showed that DoRothEA is also applicable to mouse data, as described in Holland et al., 2019. Accordingly, we included new parameters to run mouse version of DoRothEA by transforming the human genes to their mouse orthologs.
Extension to single-cell RNA-seq data: We showed that DoRothEA can be applied to scRNA-seq data, as described in Holland et al., 2020
Extension to other databases We have released a new literature based GRN with increased coverage and better performance at identifying perturbed TFs, called CollecTRI. We encourage users to use CollecTRI instead of DoRothEA. Vignettes on how to obtain activities are available at the decoupleR package.
DoRothEA is intended only for academic use as in contains resources whose licenses don’t permit commercial use. However, we developed a non-academic version of DoRothEA by removing those resources (mainly TRED from the curated databases). You can find the non-academic package with the regulons here.
If you use the DoRothEA resource, please cite:
Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research. 2019. DOI: 10.1101/gr.240663.118.
If you infer TF activities, please cite:
Badia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov D., Müller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O. and Saez-Rodriguez J. decoupleR: Ensemble of computational methods to infer biological activities from omics data. 2022. Bioinformatics Advances. DOI: 10.1093/bioadv/vbac016
If you use the CollecTRI resource, please cite:
Müller-Dott S., Tsirvouli E., Vázquez M., Ramirez Flores R.O., Badia-i-Mompel P., Fallegger R., Lægreid A. and Saez-Rodriguez J. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. bioRxiv. 2023. DOI: 10.1101/2023.03.30.534849