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Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks
The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software na...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726425/ https://www.ncbi.nlm.nih.gov/pubmed/26797014 http://dx.doi.org/10.1038/srep19598 |
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author | Wang, Yiheng Liu, Tong Xu, Dong Shi, Huidong Zhang, Chaoyang Mo, Yin-Yuan Wang, Zheng |
author_facet | Wang, Yiheng Liu, Tong Xu, Dong Shi, Huidong Zhang, Chaoyang Mo, Yin-Yuan Wang, Zheng |
author_sort | Wang, Yiheng |
collection | PubMed |
description | The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/. |
format | Online Article Text |
id | pubmed-4726425 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47264252016-01-27 Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks Wang, Yiheng Liu, Tong Xu, Dong Shi, Huidong Zhang, Chaoyang Mo, Yin-Yuan Wang, Zheng Sci Rep Article The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/. Nature Publishing Group 2016-01-22 /pmc/articles/PMC4726425/ /pubmed/26797014 http://dx.doi.org/10.1038/srep19598 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Wang, Yiheng Liu, Tong Xu, Dong Shi, Huidong Zhang, Chaoyang Mo, Yin-Yuan Wang, Zheng Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks |
title | Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks |
title_full | Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks |
title_fullStr | Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks |
title_full_unstemmed | Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks |
title_short | Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks |
title_sort | predicting dna methylation state of cpg dinucleotide using genome topological features and deep networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726425/ https://www.ncbi.nlm.nih.gov/pubmed/26797014 http://dx.doi.org/10.1038/srep19598 |
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