Introduction: N6-methyladenosine (m6A) 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 m6A sites in different species, none of them
were optimized for intronic m6A 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 m6A sites prediction approaches is limited for intronic m6A sites
Methodology: A computational framework, WITMSG, dedicated to the large-scale prediction of intronic
m6A RNA methylation sites in humans has been proposed here for the first time. Based on the
random forest algorithm and using only known intronic m6A 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 m6A sites.
Results and Conclusion: It has been observed that WITMSG outperformed competing approaches
(trained with all the m6A sites or intronic m6A 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 m6A sites, and the prediction results are freely accessible at