Abstract

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N⁶-methyladenosine (m⁶A) 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 propose here a prediction framework WHITSLE for transcriptome-wide m6A 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 m6A site prediction (average AUC: 0.960 and 0.895 under the full transcript or mature mRNA models, respectively) compared to the state-of-the-art computational approaches MethyRNA (AUC: 0.830 and 0.772) and SRAMP (AUC: 0.835 and 0.794). It also out-performed the existing epitranscriptome databases MeT-DB (AUC: 0.833 and 0.782) and RMBase (AUC: 0.822 and 0.775), which were built upon hundreds of epitranscriptome high-throughput sequencing samples. To unveil the putative biological processes impacted by changes in an individual m6A site, a network-based approach was implemented according to the ¡°guilt-by-association¡± principle by integrating the RNA methylation profile, gene expression profile and protein-protein interaction data. Finally, the WHISTLE web server was built to facilitate the query of the high-accuracy map of the human m6A epitranscriptome, and the server is available at: http://whistle-epitranscriptome.com

General results:

Citation

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Please cite WHISTLE by including the following reference in your manuscript:

Kunqi Chen^#, Zhen Wei^#, Qing Zhang^#, Xiangyu Wu^#, Rong Rong, Zhiliang Lu, Jionglong Su, Joao Pedro de Magalhaes, Daniel Rigden and Jia Meng^*. "WHISTLE: a functionally annotated high-accuracy map of the human N6-methyladenosine (m⁶A) epitranscriptome predicted from machine learning", Nucleic Acids Research, 2019.