Deogen is a variant-effect predictor that aims at the multi-level contextualization of both the target variant and the affected protein. It performs this contextualization by combining different sources of biological information. The method has been developed by Daniele Raimondi, Andrea Gazzo, Marianne Rooman, Tom Lenaerts and Wim Vranken and has been published here (doi: 10.1093/bioinformatics/btw094).
In order to perform this data integration, multiple sources of information must be queried and this complicates the Deogen pipeline (and its installation). To overcome this problem, we now provide a dockerized version of Deogen, available from our repository on DockerHub.
Using Deogen as a docker container is as simple as it gets. Only three steps are needed:
2) Download Deogen from the repository with the following command:
docker pull eddiewrc/deogen1
3) Download this python script that will help simplify your interaction with Deogen docker image.
To run deogen on your set of variants, just arrange you variants in this format and run Deogen on you variants by typing:
python deogenAutoverse.py INPUT_FILE -cpus NUMBER_CPUS
The results will be stored in a newly created DEOGEN_RESULTS folder.
Troubles running Deogen? Write us!
In order to provide the best tool possible, we are very grateful for bug reports or general comments!
How to cite Deogen:
Raimondi, D., Gazzo, A. M., Rooman, M., Lenaerts, T., & Vranken, W. F. (2016). Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects. Bioinformatics, btw094.