Abstract
5-methylcytosine (m5C) is a common nucleobase modification, and recent investigations have indicated its prevalence in cellular RNAs including mRNA, tRNA and rRNA. With the rapid accumulation of m5C sites data, it becomes not only feasible but also important to build an accurate model to predict m5C sites in silico. For this purpose, here, we developed a web-server named RNAm5Cfinder based on RNA sequence features and machine learning method to predict RNA m5C sites in eight tissue/cell types from mouse and human. We confirmed the accuracy and usefulness of RNAm5Cfinder by independent tests, and the results show that the comprehensive and cell-specific predictors could pinpoint the generic or tissue-specific m5C sites with the Area Under Curve (AUC) no less than 0.77 and 0.87, respectively. RNAm5Cfinder web-server is freely available at http://www.rnanut.net/rnam5cfinder .