scholarly journals Illuminating Potential Technical Artifacts of DNA-Methylation Array Probes

2012 ◽  
Vol 91 (4) ◽  
pp. 760-762 ◽  
Author(s):  
John D. Blair ◽  
E. Magda Price
Keyword(s):  
Dna Methylation ◽  
Methylation Array ◽  
Technical Artifacts ◽  
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 MBRS-14. INTEGRATING CLINICAL AND GENOMIC CHARACTERISTICS IN PEDIATRIC MEDULLOBLASTOMA SUBTYPES IN A SINGLE COHORT IN TAIWAN

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii400-iii401
Author(s):  
Kuo-Sheng Wu ◽  
Tai-Tong Wong
Keyword(s):  
Dna Methylation ◽  
Cluster Analysis ◽  
Treatment Strategies ◽  
Clinical Results ◽  
Tumor Location ◽  
Molecular Subgroups ◽  
Methylation Array ◽  
Metastatic Rate ◽  
Pediatric Medulloblastoma ◽  
Dna Methylation Array

Abstract BACKGROUND Medulloblastoma (MB) was classified to 4 molecular subgroups: WNT, SHH, group 3 (G3), and group 4 (G4) with the demographic and clinical differences. In 2017, The heterogeneity within MB was proposed, and 12 subtypes with distinct molecular and clinical characteristics. PATIENTS AND METHODS: PATIENTS AND METHODS We retrieved 52 MBs in children to perform RNA-Seq and DNA methylation array. Subtype cluster analysis performed by similarity network fusion (SNF) method. With clinical results and molecular profiles, the characteristics including age, gender, histological variants, tumor location, metastasis status, survival, cytogenetic and genetic aberrations among MB subtypes were identified. RESULTS In this cohort series, 52 childhood MBs were classified into 11 subtypes by SNF cluster analysis. WNT tumors shown no metastasis and 100% survival rate. All WNT tumors located on midline in 4th ventricle. Monosomy 6 presented in WNT α, but not in β subtype. SHH α and β occurred in children, while SHH γ in infant. Among SHH tumors, α subtype showed the worst outcome. G3 γ showed the highest metastatic rate and worst survival associated with MYC amplification. G4 α has the highest metastatic rate, however G4 γ showed the worst survival. CONCLUSION We identified molecular subgroups and subtypes of MBs based on gene expression and DNA methylation profile in children in our cohort series. The results may contribute to the establishment of nation-wide correlated optimal diagnosis and treatment strategies for MBs in infant and children.

Abstract LB-176: A novel high density DNA methylation array with single CpG site resolution

2011 ◽  
Author(s):  
Marina Bibikova ◽  
Bret Barnes ◽  
Chan Tsan ◽  
Vincent Ho ◽  
Brandy Klotzle ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Density ◽  
Methylation Array ◽  
Cpg Site ◽  
Dna Methylation Array

scholarly journals DNA methylation array analysis identifies breast cancer associated RPTOR, MGRN1 and RAPSN hypomethylation in peripheral blood DNA

Oncotarget ◽  
2016 ◽  
Vol 7 (39) ◽  
pp. 64191-64202 ◽  
Author(s):  
Qiuqiong Tang ◽  
Tim Holland-Letz ◽  
Alla Slynko ◽  
Katarina Cuk ◽  
Frederik Marme ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Peripheral Blood ◽  
Array Analysis ◽  
Methylation Array ◽  
Dna Methylation Array

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 An analytical pipeline for DNA Methylation Array Biomarker Studies

2021 ◽  
Author(s):  
Jennifer Lu ◽  
Darren Korbie ◽  
Matt Trau
Keyword(s):  
Dna Methylation ◽  
Feature Selection ◽  
Differential Methylation ◽  
The Cancer Genome Atlas ◽  
Methylation Array ◽  
Epigenetic Biomarkers ◽  
Cancer Genome Atlas ◽  
Dna Methylation Array ◽  
Methylation Patterns ◽  
Selection Of

DNA methylation is one of the most commonly studied epigenetic biomarkers, due to its role in disease and development. The Illumina Infinium methylation arrays still remains the most common method to interrogate methylation across the human genome, due to its capabilities of screening over 480, 000 loci simultaneously. As such, initiatives such as The Cancer Genome Atlas (TCGA) have utilized this technology to examine the methylation profile of over 20,000 cancer samples. There is a growing body of methods for pre-processing, normalisation and analysis of array-based DNA methylation data. However, the shape and sampling distribution of probe-wise methylation that could influence the way data should be examined was rarely discussed. Therefore, this article introduces a pipeline that predicts the shape and distribution of normalised methylation patterns prior to selection of the most optimal inferential statistics screen for differential methylation. Additionally, we put forward an alternative pipeline, which employed feature selection, and demonstrate its ability to select for biomarkers with outstanding differences in methylation, which does not require the predetermination of the shape or distribution of the data of interest. Availability: The Distribution test and the feature selection pipelines are available for download at: https://github.com/uqjlu8/DistributionTest Keywords: DNA methylation, Biomarkers, Cancers, Data Distribution, TCGA, 450K

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.

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