When citing Effectidor please refer to:

1. 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
2. Effectidor II: A pan-genomic AI-based algorithm for the prediction of type III secretion system effectors
   Wagner N., Baumer E., Lyubman I., Shimony Y., Bracha N., Martins L., Potnis N., Chang J.H., Teper D., Koebnik R., & Pupko T. (2025)
   Bioinformatics | https://doi.org/10.1093/bioinformatics/btaf272

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 v2.10: v2 is the OGs version, and 10 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.