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. 2019 Apr 23;47(7):e41.
doi: 10.1093/nar/gkz074.

WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach

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WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach

Kunqi Chen et al. Nucleic Acids Res. .

Abstract

N 6-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 report here a prediction framework WHISTLE 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: 0.948 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 m6A 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 m6A epitranscriptome, and the server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com.

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Figures

Figure 1.
Figure 1.
Generation of positive and negative data. The transcriptome m6A sites under RRACH consensus motifs that have been reproduced in at least two of the five training datasets were used as positive m6A sites. The negative training data (non-m6A sites) was randomly selected from the non-positive RRACH adenosines on the full transcripts containing the positive sites.
Figure 2.
Figure 2.
WHISTLE website. The WHISTLE website hosts a functionally annotated high-accuracy predicted map of the human m6A epitranscriptome. The WHISTLE website supports direct query of RNA-methylation sites with respect to a specific GO function or gene. The m6A RNA-methylation sites were predicted from m6A-CLIP data, miCLIP data, sequence features and genome-derived features. And then, the most dynamic RNA-methylation sites were annotated under the Gene Ontology framework using the guilt-by-association principle by integrating gene expression, RNA methylation and protein–protein interaction data. Please Supplementary Figure S2 for the complete data processing pipeline of WHISTLE.

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