Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE15801 | GEO | 2009/04/28
SECONDARY ACCESSION(S): PRJNA117019
REPOSITORIES: GEO
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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.
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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.
2011-10-17 | GSE27940 | GEO
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.
2009-04-28 | GSE15747 | GEO
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.
2009-04-28 | GSE15746 | GEO
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
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
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
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
Project description:To study whether methylated CpG-island (CGI) amplification coupled with microarray (MCAM) can be used to generate DNA (deoxyribonucleic acid) methylation profiles from single human blastocysts.A pilot microarray study with methylated CpG-island amplification applied to human blastocyst genomic DNA and hybridized on CpG-island microarrays.University research laboratory.Five cryopreserved sibling 2-pronuclear zygotes that were surplus to requirements for clinical treatment by in vitro fertilization were donated with informed consent from a patient attending Bourn Hall Clinic, Cambridge, United Kingdom.None.Successful generation of genome-wide DNA methylation profiles at CpG islands from individual human blastocysts, with common genomic regions of DNA methylation identified between embryos.Between 472 and 734 CpG islands were methylated in each blastocyst, with 121 CpG islands being commonly methylated in all 5 blastocysts. A further 159 CGIs were commonly methylated in 4 of the 5 tested blastocysts. Methylation was observed at a number of CGIs within imprinted-gene, differentially methylated regions (DMRs), including placental and preimplantation-specific DMRs.The MCAM method is capable of providing comprehensive DNA methylation data in individual human blastocysts.
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Project description: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.
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