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

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.