Paper Info
Title
Characterizing RNA Pseudouridylation by Convolutional Neural Networks.
Abstract
Pseudouridine (Ψ) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Ψs (especially in mRNAs) still remain largely unclear. The landscape of Ψs across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Ψ sites and characterize the contextual sequence features of pseudouridylation. PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Ψ sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.
Year
DOI
Venue
2021
10.1016/j.gpb.2019.11.015
Genomics, Proteomics & Bioinformatics
Keywords
DocType
Volume
Convolution neural network,Pseudouridylation,RNA stability,Sequence motif,Translation
Journal
19
Issue
ISSN
Citations 
5
1672-0229
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Xuan He100.68
Sai Zhang221.73
Zhang Yanqing397.68
Zhixin Lei400.34
Tao Jiang51809155.32
Jianyang Zeng613516.82