scholarly journals Review of processing and analysis methods for DNA methylation array data

2013 ◽  
Vol 109 (6) ◽  
pp. 1394-1402 ◽  
Author(s):  
C S Wilhelm-Benartzi ◽  
D C Koestler ◽  
M R Karagas ◽  
J M Flanagan ◽  
B C Christensen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Array ◽  
Array Data ◽  
Analysis Methods ◽  
Dna Methylation Array

scholarly journals EWAS Data Hub: a resource of DNA methylation array data and metadata

2019 ◽  
Vol 48 (D1) ◽  
pp. D890-D895 ◽  
Author(s):  
Zhuang Xiong ◽  
Mengwei Li ◽  
Fei Yang ◽  
Yingke Ma ◽  
Jian Sang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Cell Types ◽  
Great Promise ◽  
Methylation Array ◽  
Array Data ◽  
The Past ◽  
Comprehensive Collection ◽  
Dna Methylation Array ◽  
Brain Parts

Abstract Epigenome-Wide Association Study (EWAS) has become an effective strategy to explore epigenetic basis of complex traits. Over the past decade, a large amount of epigenetic data, especially those sourced from DNA methylation array, has been accumulated as the result of numerous EWAS projects. We present EWAS Data Hub (https://bigd.big.ac.cn/ewas/datahub), a resource for collecting and normalizing DNA methylation array data as well as archiving associated metadata. The current release of EWAS Data Hub integrates a comprehensive collection of DNA methylation array data from 75 344 samples and employs an effective normalization method to remove batch effects among different datasets. Accordingly, taking advantages of both massive high-quality DNA methylation data and standardized metadata, EWAS Data Hub provides reference DNA methylation profiles under different contexts, involving 81 tissues/cell types (that contain 25 brain parts and 25 blood cell types), six ancestry categories, and 67 diseases (including 39 cancers). In summary, EWAS Data Hub bears great promise to aid the retrieval and discovery of methylation-based biomarkers for phenotype characterization, clinical treatment and health care.

scholarly journals Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kipoong Kim ◽  
Hokeun Sun
Keyword(s):  
Dna Methylation ◽  
Biological Network ◽  
Association Studies ◽  
Genetic Network ◽  
High Dimensional ◽  
Methylation Array ◽  
Array Data ◽  
Network Information ◽  
Dna Methylation Array ◽  
Control Association

Abstract Background In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other. Results We propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. Conclusions The proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods.

scholarly journals MADA: a web service for analysing DNA methylation array data

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Xinyu Hu ◽  
Li Tang ◽  
Linconghua Wang ◽  
Fang-Xiang Wu ◽  
Min Li
Keyword(s):  
Dna Methylation ◽  
Data Analysis ◽  
Web Service ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Methylation Array ◽  
Array Data ◽  
Dna Methylation Array ◽  
Downstream Analysis ◽  
Differential Methylation Analysis

Abstract Background DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data. Results In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA. Conclusions With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA

Computational Analysis of Pathways Enrichment in DNA Methylation Array Data

2014 ◽  
pp. 144-147
Author(s):  
A. Malousi ◽  
N. Papakonstantinou ◽  
M. Tsagiopoulou ◽  
K. Stamatopoulos ◽  
N. Maglaveras
Keyword(s):  
Dna Methylation ◽  
Computational Analysis ◽  
Methylation Array ◽  
Array Data ◽  
Dna Methylation Array

scholarly journals GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data

2017 ◽  
Vol 33 (12) ◽  
pp. 1870-1872 ◽  
Author(s):  
Elior Rahmani ◽  
Reut Yedidim ◽  
Liat Shenhav ◽  
Regev Schweiger ◽  
Omer Weissbrod ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Throughput ◽  
Methylation Array ◽  
Array Data ◽  
Dna Methylation Array ◽  
User Friendly

scholarly journals MethylToSNP: identifying SNPs in Illumina DNA methylation array data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Brenna A. LaBarre ◽  
Alexander Goncearenco ◽  
Hanna M. Petrykowska ◽  
Weerachai Jaratlerdsiri ◽  
M. S. Riana Bornman ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Data Interpretation ◽  
Data Sets ◽  
Methylation Data ◽  
Methylation Array ◽  
Array Data ◽  
Functional Regions ◽  
A Genome ◽  
Probe Site ◽  
Dna Methylation Array

Abstract Background Current array-based methods for the measurement of DNA methylation rely on the process of sodium bisulfite conversion to differentiate between methylated and unmethylated cytosine bases in DNA. In the absence of genotype data this process can lead to ambiguity in data interpretation when a sample has polymorphisms at a methylation probe site. A common way to minimize this problem is to exclude such potentially problematic sites, with some methods removing as much as 60% of array probes from consideration before data analysis. Results Here, we present an algorithm implemented in an R Bioconductor package, MethylToSNP, which detects a characteristic data pattern to infer sites likely to be confounded by polymorphisms. Additionally, the tool provides a stringent reliability score to allow thresholding on SNP predictions. We calibrated parameters and thresholds used by the algorithm on simulated and real methylation data sets. We illustrate findings using methylation data from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent) and KhoeSan (southern African) populations. Our polymorphism predictions made using MethylToSNP have been validated through SNP databases and bisulfite and genomic sequencing. Conclusions The benefits of this method are threefold. First, it prevents extensive data loss by considering only SNPs specific to the individuals in the study. Second, it offers the possibility to identify new polymorphisms in samples for which there is little known about the genetic landscape. Third, it identifies variants as they exist in functional regions of a genome, such as in CTCF (transcriptional repressor) sites and enhancers, that may be common alleles or personal mutations with potential to deleteriously affect genomic regulatory activities. We demonstrate that MethylToSNP is applicable to the Illumina 450K and Illumina 850K EPIC array data and is also backwards compatible to the 27K methylation arrays. Going forward, this kind of nuanced approach can increase the amount of information derived from precious data sets by considering samples of the project individually to enable more informed decisions about data cleaning.

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