Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

High resolution detection and analysis of CpG dinucleotides methylation using MBD-Seq technology.

ABSTRACT: Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution.

SUBMITTER: Lan X

PROVIDER: S-EPMC3136941 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation.

Project description:Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non-associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25,000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter-associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species.

| S-EPMC2709589 | biostudies-literature

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection

Project description:This SuperSeries is composed of the SubSeries listed below.

2009-04-28 | GSE15801 | GEO

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection

Project description:This SuperSeries is composed of the following subset Series: GSE15746: Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 1 GSE15747: Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 2 Refer to individual Series

2010-05-19 | E-GEOD-15801 | biostudies-arrayexpress

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 2

2009-04-28 | GSE15747 | GEO

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 1

2009-04-28 | GSE15746 | GEO

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 3.

2011-10-17 | GSE27940 | GEO

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 2

Project description:Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25,000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species. We have developed a method to profile genome wide methylation. 7 ovarian normal samples and 11 tumor samples from other individuals were analyzed for CpG methylation. After inter array normalization, the tumor samples were taken together and the methylation compared to that of the normal samples to identify regions of the CpG islands that are significantly altered between the two datasets. Some of these regions were validated for their methylation as a proof of principle for the method.

2010-05-19 | E-GEOD-15747 | biostudies-arrayexpress

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 1

Project description:Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25,000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species. We have developed a method to profile genome wide methylation. 12 breast normal samples and matching tumors from these individuals and an additional 28 tumor samples from other individuals were analyzed for CpG methylation. After inter array normalization, the tumor samples were taken together and the methylation compared to that of the normal samples to identify regions of the CpG islands that are significantly altered between the two datasets. Some of these regions were validated for their methylation as a proof of principle for the method.

2009-04-28 | E-GEOD-15746 | biostudies-arrayexpress

Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 3.

Project description:The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We provide a bioinformatic analysis of copy number variation and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We individually examined the copy number variation and DNA methylation for 44 primary ovarian cancer samples and 7 ovarian normal samples using our MOMA-ROMA technology and Affymetrix expression data as well as 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with significantly altered copy number and correlated changes in expression. We identify changes in DNA methylation and expression for all amplified and deleted genes. We predicted 615 potential oncogenes and tumor suppressors candidates by integrating these multiple genomic and epigenetic data types. We have developed a method to profile genome wide methylation. 7 ovarian normal samples and 44 tumor samples from other individuals were analyzed for CpG methylation. After inter array normalization, the tumor samples were taken together and the methylation compared to that of the normal samples to identify regions of the CpG islands that are significantly altered between the two datasets. Some of these regions were validated for their methylation as a proof of principle for the method. Kamalakaran S., et. al. Mol Oncol. 2011;5:77-92 (PMID: 21169070).

2011-10-16 | E-GEOD-27940 | biostudies-arrayexpress

Global analysis of methylation profiles from high resolution CpG data.

Project description:New high throughput technologies are now enabling simultaneous epigenetic profiling of DNA methylation at hundreds of thousands of CpGs across the genome. A problem of considerable practical interest is identification of large scale, global changes in methylation that are associated with environmental variables, clinical outcomes, or other experimental conditions. However, there has been little statistical research on methods for global methylation analysis using technologies with individual CpG resolution. To address this critical gap in the literature, we develop a new strategy for global analysis of methylation profiles using a functional regression approach wherein we approximate either the density or the cumulative distribution function (CDF) of the methylation values for each individual using B-spline basis functions. The spline coefficients for each individual are allowed to summarize the individual's overall methylation profile. We then test for association between the overall distribution and a continuous or dichotomous outcome variable using a variance component score test that naturally accommodates the correlation between spline coefficients. Simulations indicate that our proposed approach has desirable power while protecting type I error. The method was applied to detect methylation differences, both genome wide and at LINE1 elements, between the blood samples from rheumatoid arthritis patients and healthy controls and to detect the epigenetic changes of human hepatocarcinogenesis in the context of alcohol abuse and hepatitis C virus infection. A free implementation of our methods in the R language is available in the Global Analysis of Methylation Profiles (GAMP) package at http://research.fhcrc.org/wu/en.html.

| S-EPMC4314375 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data