Predict Epitranscriptome Targets and Regulatory Functions of N6-Methyladenosine (m6A) Writers and Erasers. (September 2019)

• Record Type: Journal Article
• Authors: Song, Yiyou; Xu, Qingru; Wei, Zhen; Zhen, Di; Su, Jionglong; Chen, Kunqi; Meng, Jia
• Is Part Of: Evolutionary bioinformatics online. Volume 15(2019)
• Publication Date: 2019-09
• Subjects: N6-methyladenosine (m6A) -- target prediction -- epitranscriptome -- random forest -- RNA methylation; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934319871290
• Languages: English
• ISSNs: 1176-9343

Abstract

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.