Deep learning modeling m6A deposition reveals the importance of downstream cis-element sequences

Zhiyuan Luo, Jiacheng Zhang, Jingyi Fei and Shengdong Ke ()
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Zhiyuan Luo: The Jackson Laboratory
Jiacheng Zhang: The University of Chicago
Jingyi Fei: The University of Chicago
Shengdong Ke: The Jackson Laboratory

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: Abstract The N6-methyladenosine (m6A) modification is deposited to nascent transcripts on chromatin, but its site-specificity mechanism is mostly unknown. Here we model the m6A deposition to pre-mRNA by iM6A (intelligent m6A), a deep learning method, demonstrating that the site-specific m6A methylation is primarily determined by the flanking nucleotide sequences. iM6A accurately models the m6A deposition (AUROC = 0.99) and uncovers surprisingly that the cis-elements regulating the m6A deposition preferentially reside within the 50 nt downstream of the m6A sites. The m6A enhancers mostly include part of the RRACH motif and the m6A silencers generally contain CG/GT/CT motifs. Our finding is supported by both independent experimental validations and evolutionary conservation. Moreover, our work provides evidences that mutations resulting in synonymous codons can affect the m6A deposition and the TGA stop codon favors m6A deposition nearby. Our iM6A deep learning modeling enables fast paced biological discovery which would be cost-prohibitive and unpractical with traditional experimental approaches, and uncovers a key cis-regulatory mechanism for m6A site-specific deposition.

Date: 2022
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DOI: 10.1038/s41467-022-30209-7

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