Characterizing RNA Pseudouridylation by Convolutional Neural Networks
- Author:
He XUAN
1
;
Zhang SAI
;
Zhang YANQING
;
Lei ZHIXIN
;
Jiang TAO
;
Zeng JIANYANG
Author Information
1. Institute for Interdisciplinary Information Sciences,Tsinghua University,Beijing 100084,China
- Keywords:
Pseudouridylation;
Convolution neural network;
Sequence motif;
Translation;
RNA stability
- From:
Genomics, Proteomics & Bioinformatics
2021;19(5):815-833
- CountryChina
- Language:Chinese
-
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 pseu-douridylation from the local sequence information.Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accu-racy,thus enabling us to further characterize the transcriptome-wide landscape ofΨ sites.We fur-ther 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 vari-ants.The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.
