Literature DB >> 26921858

Predicting CpG methylation levels by integrating Infinium HumanMethylation450 BeadChip array data.

Shicai Fan1, Kang Huang2, Rizi Ai3, Mengchi Wang3, Wei Wang4.   

Abstract

The Infinium HumanMethylation450 BeadChip array, referred as 450K array hereinafter, has been widely adopted as an affordable technique to determine DNA methylation. Tens of thousands of data have been generated on diverse cell types and patient tissues, which have provided great insight into understanding the crucial roles of epigenetic modifications in many biological processes and diseases. The limitation of this technique is its coverage, which measures methylation levels of about 450,000 CpGs, accounting for about 1.6% of all CpGs in the human genome. In the present study we developed and compared computational models to significantly expand the coverage of Illumina 450K (~11 folds). Using the whole genome bisulfite sequencing and Illumina 450K data in the human H1 embryonic stem cell, we showed that the predicted and measured methylation levels were well correlated. Our proposed model showed superior prediction accuracies compared to the existing methods on the same dataset. When applied to predict the DNA methylome on other cells, our proposed model achieved comparable performance in cross-validations, which indicates the generalizibility of the method. Our method would thus be invaluable to maximize the usage of the existing data.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  450K array data; CpG loci; DNA methylation; Prediction

Mesh:

Year:  2016        PMID: 26921858     DOI: 10.1016/j.ygeno.2016.02.005

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  9 in total

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Authors:  Haoyang Zeng; David K Gifford
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2.  DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes.

Authors:  Milos Pavlovic; Pradipta Ray; Kristina Pavlovic; Aaron Kotamarti; Min Chen; Michael Q Zhang
Journal:  Bioinformatics       Date:  2017-10-01       Impact factor: 6.937

3.  BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues.

Authors:  Luli S Zou; Michael R Erdos; D Leland Taylor; Peter S Chines; Arushi Varshney; Stephen C J Parker; Francis S Collins; John P Didion
Journal:  BMC Genomics       Date:  2018-05-23       Impact factor: 3.969

4.  Genome-Wide Screening of Aberrant Methylation Loci for Nonsyndromic Cleft Lip.

Authors:  Xiao-Yan Xu; Xiao-Wei Wei; Wei Ma; Hui Gu; Dan Liu; Zheng-Wei Yuan
Journal:  Chin Med J (Engl)       Date:  2018-09-05       Impact factor: 2.628

5.  Statistical methods for classification of 5hmC levels based on the Illumina Inifinium HumanMethylation450 (450k) array data, under the paired bisulfite (BS) and oxidative bisulfite (oxBS) treatment.

Authors:  Alla Slynko; Axel Benner
Journal:  PLoS One       Date:  2019-06-13       Impact factor: 3.240

6.  Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers.

Authors:  Shicai Fan; Jianxiong Tang; Nan Li; Ying Zhao; Rizi Ai; Kai Zhang; Mengchi Wang; Wei Du; Wei Wang
Journal:  NPJ Genom Med       Date:  2019-02-01       Impact factor: 8.617

7.  Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods.

Authors:  Hao Xue; Zhen Wei; Kunqi Chen; Yujiao Tang; Xiangyu Wu; Jionglong Su; Jia Meng
Journal:  Evol Bioinform Online       Date:  2020-07-20       Impact factor: 1.625

8.  PretiMeth: precise prediction models for DNA methylation based on single methylation mark.

Authors:  Jianxiong Tang; Jianxiao Zou; Xiaoran Zhang; Mei Fan; Qi Tian; Shuyao Fu; Shihong Gao; Shicai Fan
Journal:  BMC Genomics       Date:  2020-05-15       Impact factor: 3.969

9.  A computational method to predict topologically associating domain boundaries combining histone Marks and sequence information.

Authors:  Wei Gan; Juan Luo; Yi Zhou Li; Jia Li Guo; Min Zhu; Meng Long Li
Journal:  BMC Genomics       Date:  2019-12-27       Impact factor: 3.969

  9 in total

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