PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm

Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desir...

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Autores principales:	Jujuan Zhuang, Danyang Liu, Meng Lin, Wenjing Qiu, Jinyang Liu, Size Chen
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	RNA modification pseudouridine site prediction feature extraction deep learning capsule network Genetics QH426-470
Acceso en línea:	https://doaj.org/article/893710d42b024260b70e1335792ffaf9
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spelling	oai:doaj.org-article:893710d42b024260b70e1335792ffaf92021-11-18T07:35:15ZPseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm1664-802110.3389/fgene.2021.773882https://doaj.org/article/893710d42b024260b70e1335792ffaf92021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.773882/fullhttps://doaj.org/toc/1664-8021Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming.Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification.Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.Jujuan ZhuangDanyang LiuMeng LinWenjing QiuWenjing QiuJinyang LiuSize ChenSize ChenSize ChenFrontiers Media S.A.articleRNA modificationpseudouridine site predictionfeature extractiondeep learningcapsule networkGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	RNA modification pseudouridine site prediction feature extraction deep learning capsule network Genetics QH426-470
spellingShingle	RNA modification pseudouridine site prediction feature extraction deep learning capsule network Genetics QH426-470 Jujuan Zhuang Danyang Liu Meng Lin Wenjing Qiu Wenjing Qiu Jinyang Liu Size Chen Size Chen Size Chen PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm
description	Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming.Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification.Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.
format	article
author	Jujuan Zhuang Danyang Liu Meng Lin Wenjing Qiu Wenjing Qiu Jinyang Liu Size Chen Size Chen Size Chen
author_facet	Jujuan Zhuang Danyang Liu Meng Lin Wenjing Qiu Wenjing Qiu Jinyang Liu Size Chen Size Chen Size Chen
author_sort	Jujuan Zhuang
title	PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm
title_short	PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm
title_full	PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm
title_fullStr	PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm
title_full_unstemmed	PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm
title_sort	pseudeep: rna pseudouridine site identification with deep learning algorithm
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/893710d42b024260b70e1335792ffaf9
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PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm

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