title: Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.
abstract: 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/.
authors.affiliation: School of Computing, University of Southern Mississippi, 118 College Drive #5106, Hattiesburg, MS 39406, USA.
authors.forename: Yiheng
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Computing, University of Southern Mississippi, 118 College Drive #5106, Hattiesburg, MS 39406, USA.
authors.forename: Tong
authors.initials: T
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, 201 Engineering Building West, Columbia, MO 65211, USA.
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.affiliation: Department of Biochemistry and Molecular Biology, Georgia Regents University, 1120 15th Street, Augusta, GA 30912, USA.
authors.forename: Huidong
authors.initials: H
authors.lastname: Shi
authors.affiliation: School of Computing, University of Southern Mississippi, 118 College Drive #5106, Hattiesburg, MS 39406, USA.
authors.forename: Chaoyang
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Pharmacology and Toxicology, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216, USA.
authors.forename: Yin-Yuan
authors.initials: YY
authors.lastname: Mo
authors.affiliation: School of Computing, University of Southern Mississippi, 118 College Drive #5106, Hattiesburg, MS 39406, USA.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Wang
mesh_terms: D045744:Cell Line, Tumor; D002877:Chromosomes, Human / Q000235:genetics; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000235:genetics*; D030541:Databases, Genetic; D015226:Dinucleoside Phosphates / Q000235:genetics*; D056426:Genetic Loci; D015894:Genome, Human; D006801:Humans; D062085:RNA, Long Noncoding / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D060388:Support Vector Machine
chemical_list: D015226:Dinucleoside Phosphates; D062085:RNA, Long Noncoding; C015772:CpG dinucleotide