Abstract
INTRODUCTION: N (6)-methyladenosine (m(6)A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m(6)A sites in different species, none of them were optimized for intronic m(6)A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m(6)A sites prediction approaches is limited for intronic m(6)A sites prediction task. METHODOLOGY: A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m(6)A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m(6)A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m(6)A sites. RESULTS AND CONCLUSION: It has been observed that WITMSG outperformed competing approaches (trained with all the m(6)A sites or intronic m(6)A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m(6)A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.