Prediction of m6A Reader Substrate Sites Using Deep Convolutional and Recurrent Neural Network

Yuxuan Wu, Yuxin Zhang, Ruoqi Wang, Jia Meng, Kunqi Chen, Yiyou Song, Daiyun Huang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

N6-methyladenosine (m6A), one of the most common post-transcriptional mRNA modifications, has been proved to correlate with multiple biological functions through the process of binding to specific m6A reader proteins. Various m6A readers exist among the genome of human beings, however, owing to the scarce wet experiments related to this topic, the binding specificity of proteins was not elucidated. Therefore, a deep learning approach combined with CNN and RNN frameworks was generated to predict the epitranscriptome-wide targets of six m6A reader proteins (YTHDF1-3, YTHDC1-2, EIF3A). Additionally, layer-wise relevance calculation was conducted to obtain each input feature contribution and tried to explain the model training process. Finally, we achieved superior performance in the classification, with an average AUROC of 0.942 in EIF3A full transcript, higher than the typical conventional machine learning algorithms (SVM) under the same condition. Moreover, we quantified the most optimal sequence length (1001bp) during the m6A reader substrate prediction. This research paves the way for further RNA methylation target prediction and functional characterization of m6A readers.

Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Biological Information and Biomedical Engineering, BIBE 2021
EditorsBin Chen
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450389297
DOIs
Publication statusPublished - 20 Jul 2021
Externally publishedYes
Event5th International Conference on Biological Information and Biomedical Engineering, BIBE 2021 - Hangzhou, China
Duration: 20 Jul 202121 Jul 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Biological Information and Biomedical Engineering, BIBE 2021
Country/TerritoryChina
CityHangzhou
Period20/07/2121/07/21

Keywords

  • Convolutional neural network
  • Deep learning
  • Readers
  • Recurrent neural network
  • m6A

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