Journal
EVOLUTIONARY BIOINFORMATICS
Volume 15, Issue -, Pages -Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1176934319871290
Keywords
N-6-methyladenosine (m(6)A); target prediction; epitranscriptome; random forest; RNA methylation
Funding
- National Natural Science Foundation of China [31671373]
- Jiangsu University Natural Science Program [16KJB180027]
- XJTLU Key Program Special Fund [KSF-T-01]
- Jiangsu Six Talent Peak Program [XYDXX-118]
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Currently, although many successful bioinformatics efforts have been reported in the epitranscriptomics field for N-6-methyladenosine (m(6)A) site identification, none is focused on the substrate specificity of different m(6)A-related enzymes, ie, the methyltransferases (writers) and demethylases (erasers). In this work, to untangle the target specificity and the regulatory functions of different RNA m6A writers (METTL3-METT14 and METTL16) and erasers (ALKBH5 and FTO), we extracted 49 genomic features along with the conventional sequence features and used the machine learning approach of random forest to predict their epitranscriptome substrates. Our method achieved reasonable performance on both the writer target prediction (as high as 0.918) and the eraser target prediction (as high as 0.888) in a 5-fold crossvalidation, and results of the gene ontology analysis of their preferential targets further revealed the functional relevance of different RNA methylation writers and erasers.
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