EpigenCentral: Portal for DNA methylation data analysis and classification in rare diseases

2020 ◽  
Vol 41 (10) ◽  
pp. 1722-1733
Author(s):  
Andrei L. Turinsky ◽  
Sanaa Choufani ◽  
Kevin Lu ◽  
Da Liu ◽  
Pouria Mashouri ◽  
...  
Epigenomics ◽  
2013 ◽  
Vol 5 (3) ◽  
pp. 301-316 ◽  
Author(s):  
Xiaotu Ma ◽  
Yi-Wei Wang ◽  
Michael Q Zhang ◽  
Adi F Gazdar

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Xuan Li ◽  
Yuejiao Fu ◽  
Xiaogang Wang ◽  
Weiliang Qiu

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 2732-2737 ◽  
Author(s):  
Zhongwei Si ◽  
Hong Yu ◽  
Zhanyu Ma

Patterns ◽  
2020 ◽  
Vol 1 (8) ◽  
pp. 100127
Author(s):  
Kun Sun ◽  
Lishi Li ◽  
Li Ma ◽  
Yu Zhao ◽  
Lin Deng ◽  
...  

Author(s):  
Islam Ibrahim Amin ◽  
Aboul Ella Hassanien ◽  
Samar K. Kassim ◽  
Hesham A. Hefny

2018 ◽  
Vol 100 ◽  
Author(s):  
Najyah A. Garoot ◽  
Byung Guk Kim

AbstractPrevious studies have generated controversial findings regarding the correlation between DNA methylation in the human genome and gene expression. Some reports have indicated that promoter methylation is negatively correlated with gene expression levels; however, in some cases, a poor or positive correlation was reported. Most previous findings were based on general trends observed with whole-genome data analysis. Here, we present a novel chromosome-specific statistical analysis design of empirical Bayes differential tests for five phases of erythroid development. To better understand the common methylation patterns of differentially methylated regions (DMRs) during specific stages, we defined differential phases for each CpG locus, based on a maximum log2 fold change. Analyzing hypermethylated and hypomethylated CpG loci separately showed variations in methylation patterns during erythropoiesis in the gene body, promoter and enhancer regions. Hypomethylated DMRs showed stronger associations with erythroid-specific enhancers at the differentiation start phase and with exons in the intermediate phase. To investigate the hypomethylated DMRs further, transcription factor binding site-enrichment analysis was conducted. This analysis highlighted novel transcription factors during each differentiation stage that were not detected by previous differential methylation data analysis. In contrast, hypermethylated DMRs showed a consistent methylation pattern over the different genomic regions. Thus, a closer examination of DNA methylation patterns in a single chromosome during each developmental stage can contribute to verify the association nature between gene expression and DNA methylation.


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Qiangwei Zhou ◽  
Jing-Quan Lim ◽  
Wing-Kin Sung ◽  
Guoliang Li

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hanyu Zhang ◽  
Ruoyi Cai ◽  
James Dai ◽  
Wei Sun

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.


2010 ◽  
Vol 20 (12) ◽  
pp. 1719-1729 ◽  
Author(s):  
M. D. Robinson ◽  
C. Stirzaker ◽  
A. L. Statham ◽  
M. W. Coolen ◽  
J. Z. Song ◽  
...  

Epigenetics ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. 333-337 ◽  
Author(s):  
Kirsten Hogg ◽  
E Magda Price ◽  
Wendy P Robinson

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