WHISTLE: a high-accuracy map of the human N(6)-methyladenosine (m(6)A) epitranscriptome predicted using a machine learning approach

N (6)-methyladenosine (m(6)A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA–protein interaction. We report here a prediction framework WHISTLE for transcriptome-wide m(6)A RNA-methylation site prediction. When tested on six independent datasets, our approach, which integrated 35 additional genomic features besides the conventional sequence features, achieved a major improvement in the accuracy of m(6)A site prediction (average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively) compared to the state-of-the-art computational approaches MethyRNA (AUC: 0.790 and 0.732) and SRAMP (AUC: 0.761 and 0.706). It also out-performed the existing epitranscriptome databases MeT-DB (AUC: 0.798 and 0.744) and RMBase (AUC: 0.786 and 0.736), which were built upon hundreds of epitranscriptome high-throughput sequencing samples. To probe the putative biological processes impacted by changes in an individual m(6)A site, a network-based approach was implemented according to the ‘guilt-by-association’ principle by integrating RNA methylation profiles, gene expression profiles and protein–protein interaction data. Finally, the WHISTLE web server was built to facilitate the query of our high-accuracy map of the human m(6)A epitranscriptome, and the server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com.