Feature Mining and Integration for Improving the Prediction Accuracy of Translation Initiation Sites in Eukaryotic mRNAs

Accurate prediction of translation initiation sites (TISs) is important for the annotation of genomes. Although many methods have been proposed to solve this problem, the prediction accuracy is still limited. In this paper, the features that have been widely used for predicting TISs are further analyzed, and it is found that some features of TISs and non-TISs are heavily dependent on the C+G content of sequences around AUG codons, and some features are quite different for non-TISs located in untranslated regions and coding regions considering different reading frames. Further, the strategy of using multiple support vector machines to fully make use of the information is proposed, and a new program TISKey for the prediction of TISs is developed. Testing results on widely used dataset demonstrate that TISKey could get better prediction accuracy. TISKey can be accessed at http://infosci.hust.edu.cn