When citing Effectidor please refer to:

Effectidor: an automated machine-learning-based web server for the prediction of type-III secretion system effectors
Wagner N., Avram O., Gold-Binshtok D., Zerah B., Teper D., & Pupko T. (2022)
Bioinformatics | https://doi.org/10.1093/bioinformatics/btac087

When including the secretion signal feature please also refer to:

Natural language processing approach to model the secretion signal of type III effectors
Wagner N., Alburquerque M., Ecker N., Dotan E., Zerah B., Mendonca Pena M., Potnis N., & Pupko T. (2022)
Frontiers in Plant Science | https://doi.org/10.3389/fpls.2022.1024405

The current version of Effectidor is v2.08: v2 is the OGs version, and 08 is the T3Es dataset version.

The source code is available at:

https://github.com/naamawagner/Effectidor

Acknowledgements:

This study was supported in part by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel Aviv University, a fellowship from the Manna Center Program for Food Safety and Security at Tel Aviv University, and a fellowship from the Dalia and Eli Hurvitz foundation. Naama Wagner, Dafna Gold-Binshtok, Ben Zerah, Doron Teper, and Tal Pupko developed the algorithmic pipeline. Naama Wagner and Oren Avram developed the web server.