T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
Xinjie Hui, Zewei Chen, Mingxiong Lin, Junya Zhang, Yueming Hu, Yingying Zeng, Xi Cheng, Le Ou-Yang, Ming-an Sun, Aaron P. White, Wang Yejun
Abstract
Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.