Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification  

作　　者：Guodong Li Bowei Zhao Xiaorui Su Dongxu Li Yue Yang Zhi Zeng Lun Hu 

机构地区：[1]Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Science,Urumqi 830011,China [2]College of Computer,Xi’an Jiaotong University,Xi’an 710049,China [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第10期2123-2134,共12页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(62373348);the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2021D01D05);the Tianshan Talent Training Program(2023TSYCLJ0021);the Pioneer Hundred Talents Program of Chinese Academy of Sciences.

摘　　要：N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

关 键 词：Cross-domain reconstruction cross-species prediction N6-methyladenosine(m6A)modification site RNA sequence sequential and structural dependencies 

分 类 号：Q811.4[生物学—生物工程] TP18[自动化与计算机技术—控制理论与控制工程]