Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA175631 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE41048: Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues (ChIP-Seq and MeDIP-Seq) GSE41049: Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues (Gene Expression data) Refer to individual Series
2012-09-24 | E-GEOD-41050 | biostudies-arrayexpress
Project description:Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues (Gene Expression data)
| PRJNA175632 | ENA
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. We show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells. Genomewide sequencing data is included herein: H3K27me3 profiled throughout the microevolutionary timecourse using ChIP-seq, DNA methylation profiled through the timecourse using MeDIP-seq, and H3K4me3 and CTCF profiled in late and early timepoints using ChIP-seq. Deep Bisulfite sequencing amplicon data and results can be obtained at: http://compgenomics.weizmann.ac.il/tanay/?page_id=99 .
2012-09-24 | E-GEOD-41048 | biostudies-arrayexpress
Project description:Compared with non-cancerous lung tissues, lung cancer in Xuanwei tissues expressed a total of 6,899 differentially methylated regions, including 5,788 hypermethylated regions and 1,111 hypomethylated regions. Many differentially methylated regions have been found in lung cancer in Xuanwei.
2018-04-21 | GSE113432 | GEO
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. We show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells. Gene expression profiled in duplicate throughout the microevolutionary timecourse using Affymetrix Gene 1.0 ST arrays.
2012-09-24 | E-GEOD-41049 | biostudies-arrayexpress
Project description:we report the partial methylome (CG-rich regions) of HEK293 cells and HEK293 cells over-expressing the BAHD1 gene (HEK-BAHD1) We used MEDIP-seq to identify genomic regions differentially methylated upon overexpression of the chromatin repressor BAHD1 in HEK293 cells.
2015-12-11 | E-GEOD-51864 | biostudies-arrayexpress
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. We show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells.
2012-09-24 | GSE41048 | GEO
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. We show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells.
2012-09-24 | GSE41049 | GEO
Project description:Purpose: MeDIP based Next-generation sequencing (NGS) has revolutionized differential and funtional mapping of genome-wide methylation signature. The goals of this study are to compare MeDIP-seq derived methylome profiling of miRNA in EOC samples and normal ovary tissue samples, and their downstream expression analysis through quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods. Methods: Six EOC tissue and two normal ovary samples were processed and genomic DNA was isolated. MeDIP-seq based library was prepared and for all eight samples and cluster generation and pair end sequencing was performed on Illumina TrueSeq 500 platform. Adptor triming, low quality read and duplicate reads were removed. High qulity read were aligned using BWA-Mem tools. Methylated genome regions and differential methylation analysis was performed using diffReps (v1.55.6). In addition, hypomethylation/hypermethylation of miRNA genes and thier downstream expression analysis was evaluated using qRT–PCR SYBR Green based assays Results: Using an optimized data analysis workflow, we mapped about ~60-80 million sequence reads per sample to the human. arround 2,24,929 DMR (p<0.05) were identified as differentially methylated regions and out of them 45% were hypermethylated and 55% were hypomethylated compared to normal samples. genomic distribution of DMRs revealed higher enrichment in Gene body, and in other intergenic region. Arround 50 proximal promoter regions of miRNA genes were hypomethylated, while 80 were fall in hypermethylated region. Out of these three miRNA from hypomethylated were screened for further qRT-PCR based expression analysis. qRT-PCR expression analysis revealed upregulation of candidate miRNA in EOC compared to normal samples. Upregulated expression profile of three miRNA obtained from qRT-PCR confiremed there association with hypomethylation of miRNA genes and their downstream expression. Conclusions: Our study revealed genome-wide methylome analysis of ovarian cancer tissue samples, using MeDIP NGS based approach. The optimized data analysis workflows reported here should provide a association between gene methylation and their downream expression profiles. Our results show that MeDIP-seq offers a comprehensive and more accurate genome-wide differential methylation analysis of ovarian cancer tissue. We conclude that MeDIP-seq based analysis would help in understanding of differential methylation characteristics of cancerous and non-cancerous ovarian tissue samples and could allow us to understand complex biological functions
2021-10-05 | GSE180292 | GEO