m 6A was detected within poly-A RNA for the first time in 1974 ( 5), and has since been characterized in various eukaryotic species. Among them, the most prevalent non-cap modification marker present on eukaryotic messenger RNA (mRNA) and long non-coding RNA, N 6-methyladenosine (m 6A), ( 3) has emerged as an abundant and dynamically regulated modification ( 4). With the recent advances in the exploration of RNA epigenetics, more than 150 types of RNA modifications have been identified ( 2). Large scale analysis has revealed the abundance of RNA modifications in the human epitranscriptome ( 1). Finally, the WHISTLE web server was built to facilitate the query of our high-accuracy map of the human m 6A epitranscriptome, and the server is freely available at: and.
To probe the putative biological processes impacted by changes in an individual m 6A 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. 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.
#WOR PC RICHARDS WHISTLE HOTLINE FULL#
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 6A 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). We report here a prediction framework WHISTLE for transcriptome-wide m 6A RNA-methylation site prediction. N 6-methyladenosine (m 6A) 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.
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