Computational prediction of type III and IV secreted effectors in gram-negative bacteria.

In this review, we provide an overview of the methods employed in four recent studies that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. We present the results of these studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the Web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVE server (http://www.