Prediction of signal peptide cleavage sites with template matching fusion algorithm

Fast and effective prediction of signal peptides and their cleavage sites is of great importance in computational biology. There are two kinds of approaches developed to predict signal peptides, one of which based on model training approach such as SignalP and SPEPlip, and another based on sliding window method such as PrediSi, Signal-CF and Signal-3L. In this paper, the scaled window method proposed by Chou was employed to extract cleavable secretory segments, and template matching fusion method (named as Signal-TMF) was introduced to predict signal peptide cleavage sites. Comparing with Chou´s Signal-CF method, the best overall accuracy of Signal-TMF is 6-15% higher than of Chou´s in jackknife test. The Signal-TMF can also be used to effectively predict the long signal peptide cleavage site. The results show that Signal-TMF will be very useful to the areas related to signal peptides.