m<SUP>6</SUP>A Reader: Epitranscriptome Target Prediction and Functional Characterization of <i>N</i><SUP>6</SUP>-Methyladenosine (m<SUP>6</SUP>A) Readers

Zhen, Di, Wu, Yuxuan, Zhang, Yuxin, Chen, Kunqi, Song, Bowen, Xu, Haiqi, Tang, Yujiao, Wei, Zhen and Meng, Jia ORCID: 0000-0003-3455-205X
(2020) m<SUP>6</SUP>A Reader: Epitranscriptome Target Prediction and Functional Characterization of <i>N</i><SUP>6</SUP>-Methyladenosine (m<SUP>6</SUP>A) Readers. FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 8. 741-.

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<i>N</i> <sup>6</sup>-methyladenosine (m<sup>6</sup>A) is the most abundant post-transcriptional modification in mRNA, and regulates critical biological functions via m<sup>6</sup>A reader proteins that bind to m<sup>6</sup>A-containing transcripts. There exist multiple m<sup>6</sup>A reader proteins in the human genome, but their respective binding specificity and functional relevance under different biological contexts are not yet fully understood due to the limitation of experimental approaches. An <i>in silico</i> study was devised to unveil the target specificity and regulatory functions of different m<sup>6</sup>A readers. We established a support vector machine-based computational framework to predict the epitranscriptome-wide targets of six m<sup>6</sup>A reader proteins (YTHDF1-3, YTHDC1-2, and EIF3A) based on 58 genomic features as well as the conventional sequence-derived features. Our model achieved an average AUC of 0.981 and 0.893 under the full-transcript and mature mRNA model, respectively, marking a substantial improvement in accuracy compared to the sequence encoding schemes tested. Additionally, the distinct biological characteristics of each individual m<sup>6</sup>A reader were explored via the distribution, conservation, Gene Ontology enrichment, cellular components and molecular functions of their target m<sup>6</sup>A sites. A web server was constructed for predicting the putative binding readers of m<sup>6</sup>A sites to serve the research community, and is freely accessible at: http://m6areader.rnamd.com.

Item Type: Article
Uncontrolled Keywords: N6-methyladenosine, m(6)A reader, machine learning (ML), YTH domain, eIF3a
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 16 Aug 2021 15:00
Last Modified: 10 Oct 2023 00:02
DOI: 10.3389/fcell.2020.00741
Open Access URL: https://www.frontiersin.org/articles/10.3389/fcell...
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3133711