circTIS: A Weighted Degree String Kernel with Support Vector Machine Tool for Translation Initiation Sites Prediction in circRNA

Abstract

Recent studies discovered that peptides generated from the translation of circRNAs participate in several biological processes, many related to human diseases. Researchers have observed that initiation of translation in circRNAs frequently occurs from non-AUG start codons. However, most existing computational tools for translation initiation site (TIS) prediction consider only the canonical AUG start codon. Thus, we developed a new methodology for predicting TIS AUG and near-cognates, considering the circularization of ORFs occurring in circRNAs. Initially, we used the weighted degree string kernel to create a data representation of the circRNA sequence fragments around possible TIS. Next, we applied a support vector machine to calculate a score representing the potential of the sequence fragment to contain an actual TIS. We used datasets from annotated TIS on circRNAs sequences to train and test our methodology. The first experiment showed that the sequence fragment length is the best value for the kernel’s degree hyperparameter. Next, we investigated the most suitable sequence fragment length. Finally, we compared our methodology with three tools, TITER, TIS Predictor, and TIS Transformer. For TIS AUG prediction, circTIS obtained an AUROC of 98.64%, while TITER, TIS Predictor, and TIS Transformer obtained 78.97%, 78.39%, and 81.3%, respectively. For the TIS near-cognate prediction, our method obtained an AUROC equal to 96.84%, while TITER, TIS Predictor, and TIS Transformer got 81.37%, 72.68%, and 66.33%, respectively. We implemented our methodology in the circTIS tool, freely available at https://github.com/denilsonfbar/circTIS.