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MRCNN: a deep learning model for regression of genome-wide DNA methylation

BACKGROUND: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted...

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Autores principales: Tian, Qi, Zou, Jianxiao, Tang, Jianxiong, Fang, Yuan, Yu, Zhongli, Fan, Shicai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6457069/
https://www.ncbi.nlm.nih.gov/pubmed/30967120
http://dx.doi.org/10.1186/s12864-019-5488-5
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author Tian, Qi
Zou, Jianxiao
Tang, Jianxiong
Fang, Yuan
Yu, Zhongli
Fan, Shicai
author_facet Tian, Qi
Zou, Jianxiao
Tang, Jianxiong
Fang, Yuan
Yu, Zhongli
Fan, Shicai
author_sort Tian, Qi
collection PubMed
description BACKGROUND: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge. Existing approaches have yielded good predictions, but the methods either need to combine plenty of features and prerequisites or deal with only hypermethylation and hypomethylation. RESULTS: In this paper, we propose a deep learning method for prediction of the genome-wide DNA methylation, in which the Methylation Regression is implemented by Convolutional Neural Networks (MRCNN). Through minimizing the continuous loss function, experiments show that our model is convergent and more precise than the state-of-art method (DeepCpG) according to results of the evaluation. MRCNN also achieves the discovery of de novo motifs by analysis of features from the training process. CONCLUSIONS: Genome-wide DNA methylation could be evaluated based on the corresponding local DNA sequences of target CpG loci. With the autonomous learning pattern of deep learning, MRCNN enables accurate predictions of genome-wide DNA methylation status without predefined features and discovers some de novo methylation-related motifs that match known motifs by extracting sequence patterns. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5488-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-64570692019-04-19 MRCNN: a deep learning model for regression of genome-wide DNA methylation Tian, Qi Zou, Jianxiao Tang, Jianxiong Fang, Yuan Yu, Zhongli Fan, Shicai BMC Genomics Research BACKGROUND: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge. Existing approaches have yielded good predictions, but the methods either need to combine plenty of features and prerequisites or deal with only hypermethylation and hypomethylation. RESULTS: In this paper, we propose a deep learning method for prediction of the genome-wide DNA methylation, in which the Methylation Regression is implemented by Convolutional Neural Networks (MRCNN). Through minimizing the continuous loss function, experiments show that our model is convergent and more precise than the state-of-art method (DeepCpG) according to results of the evaluation. MRCNN also achieves the discovery of de novo motifs by analysis of features from the training process. CONCLUSIONS: Genome-wide DNA methylation could be evaluated based on the corresponding local DNA sequences of target CpG loci. With the autonomous learning pattern of deep learning, MRCNN enables accurate predictions of genome-wide DNA methylation status without predefined features and discovers some de novo methylation-related motifs that match known motifs by extracting sequence patterns. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5488-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-04 /pmc/articles/PMC6457069/ /pubmed/30967120 http://dx.doi.org/10.1186/s12864-019-5488-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Tian, Qi
Zou, Jianxiao
Tang, Jianxiong
Fang, Yuan
Yu, Zhongli
Fan, Shicai
MRCNN: a deep learning model for regression of genome-wide DNA methylation
title MRCNN: a deep learning model for regression of genome-wide DNA methylation
title_full MRCNN: a deep learning model for regression of genome-wide DNA methylation
title_fullStr MRCNN: a deep learning model for regression of genome-wide DNA methylation
title_full_unstemmed MRCNN: a deep learning model for regression of genome-wide DNA methylation
title_short MRCNN: a deep learning model for regression of genome-wide DNA methylation
title_sort mrcnn: a deep learning model for regression of genome-wide dna methylation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6457069/
https://www.ncbi.nlm.nih.gov/pubmed/30967120
http://dx.doi.org/10.1186/s12864-019-5488-5
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