Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data
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ABSTRACT:
PROVIDER: EGAS00001003504 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:We describe an optimized microarray method for identifying genome-wide CpG island methylation called Microarray-based Methylation Assessment of Single Samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimised combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared to a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison to previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation. Keywords: Methylation genomic hybridizations.
2006-10-31 | GSE5326 | GEO
Project description:Lineage reconstruction is central to understanding tissue development and maintenance. While powerful tools to infer cellular relationships have been developed, these methods typically have a clonal resolution and require a transgene. Here, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single cell-division resolution using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy, with the ability to infer larger lineage trees depending on the distribution of 5hmC patterns. Finally, we show that scPECLR can also be used to test the "immportal strand" hypothesis in stem cell biology. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual cell-division resolution.
2021-07-08 | GSE131678 | GEO
Project description:Gene set analysis as it is typically applied to genome-wide methylation assays is severely biased as a result of differences in the numbers and sizes of CpG islands associated with different classes of genes. We demonstrate this bias using published data from a study of differential methylation in lung cancer and a data set we generated to study methylation changes in patients with long-standing ulcerative colitis and show that several of the gene sets that appear enriched would also be identified with randomized data. We also report a method to correct the bias. Application of the corrected method to the lung cancer and ulcerative colitis data sets provides novel and potentially interesting biological insights into the role of methylation in cancer development and chronic inflammation. We used Agilent Human CpG Island microarrays to compare methylation patterns in sigmoid colon tissue between five individuals suffering from ulcerative colitis and five healthy age-matched controls.
2012-07-12 | E-GEOD-39188 | biostudies-arrayexpress
Project description:DNA methylation and nucleosome positioning work together to generate chromatin structures that regulate gene expression. Nucleosomes are typically mapped using nuclease digestion requiring significant amounts of material and varying enzyme concentrations. We have developed a method (NOMe-seq) that uses a GpC methyltransferase (M.CviPI) and next generation sequencing to generate a high resolution footprint of nucleosome positioning genome-wide using less than 1 million cells while retaining endogenous DNA methylation information from the same DNA strand. Using a novel bioinformatics pipeline we show a striking anti-correlation between nucleosome occupancy and DNA methylation at CTCF regions, that is not present at promoters. We further show that the extent of nucleosome depletion at promoters is directly correlated to expression level and can accommodate multiple nucleosomes and provide genome-wide evidence that expressed non-CpG island promoters are nucleosome depleted. Importantly, NOMe-seq obtains DNA methylation and nucleosome positioning information from the same DNA molecule, giving the first genome-wide DNA methylation and nucleosome positioning correlation at the single molecule and thus, single cell level that can be used to monitor disease progression and response to therapy. Nucleosome Occupancy and Methylome-Sequencing (NOMe-Seq) on IMR90 cell line and 2 GBM cell lines
2012-09-12 | E-GEOD-40770 | biostudies-arrayexpress
Project description:This set of data is for assessing and validation of a novel method, CGI-seq, as described in the corresponding paper, for profiling of single cell CpG methylome. We tested 4 different cell lines (K562, GM12878, 551 K1-iPSC, and Fibroblast ATCC-CCL-110) with a low quantity of cells (LQC, here including 100-cell or 10-cell) and with single cells (SC) separately. Briefly, the protocol include these steps. The intact gDNA of the cell(s) was released. This gDNA (i.e. the TEST sample, but this step was omitted for the Methylation Control or MC sample) was cut with the 1st set of restriction endonucleases, called RE1, which were sensitive to CpG methylation, and distinguished methylated from unmethylated DNA sequences. Then, each sample, including digested TEST DNAs and intact MC DNA, was immediately amplified by MDA method (or REPLIg kit), in which the methylated DNA sequences were highly efficiently amplified, while unmethylated DNA sequences were depleted. The amplicon was then cut with the 2nd set of restriction endonucleases, called RE2, and the appropriate size (between 50-500bp) of fragments were recovered to generate sequencing library, which was to enrich CpG-rich sequences (especially CpG islands) and improve the sequencing efficiency (reduced representation sequencing). Finally, Next Generation Sequencing (NGS) was performed. The outcome sequencing reads were analyzed with a specially designed algorithms. The result shows the high sensitivity, high reliability, high reproduciblity, and high coverage of the method on profiling the genome-wide CpG methylation pattern for single cells. It is a simple, robust and faithful technology for single cell CpG methylation pattern analysis, enabling unsupervised clustering of single cells basing on genome-wide CpG methylation pattern. This method will be valuable to dissect the subpopulation structure for a cellular population heterogeneous in CpG-methylation pattern.
2017-02-12 | GSE75346 | GEO
Project description:DNA methylation at a gene promoter region has the potential to regulate gene transcription. Their patterns are often complex with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). The methylation state at adjacent CpG sites is therefore lost when data is summarised this way. Methylation patterns can only be characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns. We have developed an analysis and visualisation software tool "Methpat", that extracts and displays clonal DNA methylation patterns from massively parallel sequencing data aligned using Bismark. We have performed multiplex bisulfite amplicon sequencing on a range of CpG island targets across a panel of human cell lines and primary tissues. Using Methpat, we demonstrate clonal diversity of epialleles analysed at specific gene promoter regions. We also describe the existence of DNA methylation within the mitochondrial genome. Multiplex bisulfite PCR and Next Generation sequencing of 35 samples
2015-11-23 | E-GEOD-67856 | biostudies-arrayexpress
Project description:Gene set analysis as it is typically applied to genome-wide methylation assays is severely biased as a result of differences in the numbers and sizes of CpG islands associated with different classes of genes. We demonstrate this bias using published data from a study of differential methylation in lung cancer and a data set we generated to study methylation changes in patients with long-standing ulcerative colitis and show that several of the gene sets that appear enriched would also be identified with randomized data. We also report a method to correct the bias. Application of the corrected method to the lung cancer and ulcerative colitis data sets provides novel and potentially interesting biological insights into the role of methylation in cancer development and chronic inflammation.
2012-07-13 | GSE39188 | GEO
Project description:We report a new method for genome-wide methylation profiling that is able to probe methylation status in both single-copy DNA and interspersed repeats. This method, MethylMAPS, uses methylation-sensitive and -dependent enzymes to fractionate the genome according to methylation state. Methylated and unmethylated fragments are then sequenced with Next-Gen sequencing to map methylated and unmethylated CpG sites in the genome. We have used this method to determine the methylation status of >275 million CpG sites in human and mouse DNA from breast and brain tissues. We conclude that methylation is the default state of most CpG dinucleotides and that a combination of local dinucleotide frequencies, the interaction of repeated sequences, and the presence or absence of histone variants or modifications shields a population of CpG sites (most of which are in and around promoters) from DNA methyltransferases that lack intrinsic sequence specificity. Genome-wide methylation mapping in two normal human breast tissues, human brain tissue and mouse brain tissue.
2010-04-16 | E-GEOD-21242 | biostudies-arrayexpress
Project description:DNA methylation at a gene promoter region has the potential to regulate gene transcription. Their patterns are often complex with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). The methylation state at adjacent CpG sites is therefore lost when data is summarised this way. Methylation patterns can only be characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns. We have developed an analysis and visualisation software tool "Methpat", that extracts and displays clonal DNA methylation patterns from massively parallel sequencing data aligned using Bismark. We have performed multiplex bisulfite amplicon sequencing on a range of CpG island targets across a panel of human cell lines and primary tissues. Using Methpat, we demonstrate clonal diversity of epialleles analysed at specific gene promoter regions. We also describe the existence of DNA methylation within the mitochondrial genome.
2015-11-23 | GSE67856 | GEO
Project description:Embryonic stem cells can be differentiated in vitro to produce a variety of somatic cell types. We have employed the mDIP method combined with hybridization to a CpG islend microarray . We show that this differentiation is accompanied by an intrinsic process of extensive aberrant CpG island de novo methylation that includes developmental and cancer target genes. CpG-methylated genomic DNA was enriched using a methyl-DNA immunoprecipitation (mDIP) assay. DNA from the input and bound (enriched) DNA for each sample were labeled and hybridized on the array to define the methylation state of each region.
2014-02-05 | E-GEOD-54664 | biostudies-arrayexpress