Predict Epitranscriptome Targets and Regulatory Functions of N(6)-Methyladenosine (m(6)A) Writers and Erasers

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 m(6)A 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 cross-validation, and results of the gene ontology analysis of their preferential targets further revealed the functional relevance of different RNA methylation writers and erasers.