MethylSig: a whole genome DNA methylation analysis pipeline
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE55569 | GEO | 2014/08/22
SECONDARY ACCESSION(S): PRJNA240058
REPOSITORIES: GEO
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Project description:DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings. Current methods to analyze genome-wide bisulfite sequencing data use a smoothing approach or a simple statistical test based on the binomial distribution. Comparative DNA methylation profiling in AML blasts and normal CD34(+) control cells
2014-08-22 | E-GEOD-55569 | biostudies-arrayexpress
Project description:DNA methylation is a reaction that results in the formation of 5-methylcytosine when a methyl group is added to the cytosine’s C5 position. As organisms age, DNA methylation patterns change in a reproducible fashion. This phenomenon has established DNA methylation as a valuable biomarker in aging studies. Epigenetic clocks based on weighted combinations of methylation sites have been developed to accurately predict the age of an individual from their methylome. However, many epigenetic clocks, particularly those that utilize penalized regression, model the changes in methylation linearly with age. Moreover, these models, which use methylation levels as features, are not robust to missing data and do not account for the count-based nature of bisulfite sequence data. Additionally, the models are generally not interpretable. To overcome these challenges, we present BayesAge, an extension of the previously developed scAge approach that was developed for the analysis of single cell DNA methylation datasets. BayesAge utilizes maximum likelihood estimation (MLE) to infer ages, models count data using binomial distributions, and uses LOWESS smoothing to capture the non-linear dynamics between methylation and age. Our approach is designed for use with bulk bisulfite sequencing datasets. BayesAge outperforms scAge in several respects. Specifically, BayesAge’s age residuals are not age associated, thus providing a less biased representation of epigenetic age variation across populations. Moreover, BayesAge enables the estimation of error bounds on age inference and, when run on downsampled data, its coefficient of determination between predicted and actual ages surpasses both scAge and penalized regression.
2024-03-20 | GSE261769 | GEO
Project description:COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data. It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values. COHCAP is currently the only DNA methylation package that can handle integration with gene expression data, and the results of this study show that COHCAP can identify regions of differential methylation with ~50% concordance with gene expression. COHCAP is scalable for analysis of both cell line data and heterogeneous patient data, and it can identify known cancer biomarkers as well as intriguing new roles of epigenetic regulation in cancer (such as methylation of estrogen receptor in breast cancer patients). This study also uses cell line data to show that COHCAP is capable of analyzing Illumina methylation array and targeted bisulfite sequencing data, with either 1-group or 2-group study designs. The accuracy of COHCAP is accessed using qPCR, EpiTect, and comparison of COHCAP regions of differential methylation with MIRA peaks. This software is freely available at https://sourceforge.net/projects/cohcap/. The following third-party datasets were utilized in the paper: BS-Seq data: GSE26826 Additional Microarray Data: GSE29290 This SuperSeries is composed of the SubSeries listed below. Refer to individual Series.
2013-06-08 | E-GEOD-42310 | biostudies-arrayexpress
Project description:We intend to establish an efficient method for plasma cfDNA extraction and Bisulfite transformation to facilitate the detection of DNA methylation status using multiplex fluorescence PCR. Meanwhile, we expect to identify several plasma methylation markers that can be highly sensitive for multi-cancer detection. Finally, we will provide a pan-cancer blood test that is easy to operate, low cost, accurate and easy to promote.
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Project description:DNA methylation plays a critical role in development, particularly in repressing retrotransposons. The mammalian methylation landscape is dependent on the combined activities of the canonical maintenance enzyme Dnmt1 and the de novo Dnmts, 3a and 3b. Here we demonstrate that Dnmt1 displays de novo methylation activity in vitro and in vivo with specific retrotransposon targeting. We used whole-genome bisulfite and long-read Nanopore sequencing in genetically engineered methylation depleted embryonic stem cells to provide an in-depth assessment and quantification of this activity. Utilizing additional knockout lines and molecular characterization, we show that Dnmt1's de novo methylation activity depends on Uhrf1 and its genomic recruitment overlaps with targets that enrich for Trim28 and H3K9 trimethylation. Our data demonstrate that Dnmt1 can de novo add and maintain DNA methylation, especially at retrotransposons and that this mechanism may provide additional stability for long-term repression and epigenetic propagation throughout development.
2021-06-29 | PXD025736 | Pride
Project description:In the study of DNA methylation, genetic variation between species, strains, or individuals can result in CpG sites that are exclusive to a subset of samples, and insertions and deletions can rearrange the spatial distribution of CpGs. How to account for this variation in an analysis of the interplay between sequence variation and DNA methylation is not well understood, especially when the number of CpG differences between samples is large. Here we use whole-genome bisulfite sequencing data on two highly divergent mouse strains to study this problem. We show that alignment to personal genomes is necessary for valid methylation quantification. We introduce a method for including strain-specific CpGs in differential analysis, and show that accounting for strain-specific CpGs increases power. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, and also allowing us to account for differences in the spatial occurrences of CpGs. Our results have implications for joint analysis of genetic variation and DNA methylation using bisulfite-converted DNA, and unlocks the use of personal genomes for addressing this question.
2019-05-14 | GSE87101 | GEO
Project description:COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data. It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values. COHCAP is currently the only DNA methylation package that can handle integration with gene expression data, and the results of this study show that COHCAP can identify regions of differential methylation with ~50% concordance with gene expression. COHCAP is scalable for analysis of both cell line data and heterogeneous patient data, and it can identify known cancer biomarkers as well as intriguing new roles of epigenetic regulation in cancer (such as methylation of estrogen receptor in breast cancer patients). This study also uses cell line data to show that COHCAP is capable of analyzing Illumina methylation array and targeted bisulfite sequencing data, with either 1-group or 2-group study designs. The accuracy of COHCAP is accessed using qPCR, EpiTect, and comparison of COHCAP regions of differential methylation with MIRA peaks. This software is freely available at https://sourceforge.net/projects/cohcap/. The following third-party datasets were utilized in the paper: BS-Seq data: GSE26826 Additional Microarray Data: GSE29290 This SuperSeries is composed of the SubSeries listed below.
2013-06-08 | GSE42310 | GEO
Project description:RNA-Sequencing is a transformative method that captures the quantitative dynamics of a transcriptome with exquisite sensitivity and single-base resolution. There are, however, few computational pipelines for RNA-Seq with statistical tests that evince sufficient robustness and power as demanded by the difficult combination of small sample sizes and high variability in sequence read counts. To this end, we developed GENE-counter, a complete software pipeline for analyzing RNA-Seq data for genome-wide expression differences between replicated treatment groups. One important component of GENE-counter is a statistical test based on the NBP parameterization of the negative binomial distribution for identifying differentially expressed genome features. We used GENE-counter to analyze RNA-Seq data derived from Arabidopsis thaliana infected with a strain of defense-eliciting bacteria. We identified 308 genes that were differentially induced. Using alternative methods, we provided support for the induced expression and biological relevance of a substantial proportion of the genes. These results suggest the NBP parameterization of the negative binomial distribution is well suited for explaining RNA-Seq data and the statistical test makes GENE-counter a powerful pipeline for studying genome-wide expression changes. GENE-counter is freely available at http://changlab.cgrb.oregonstate.edu/. Our RNA-seq data is uploaded on the NCBI short read archive (SRA) under the SRA025952.
2010-12-03 | GSE25818 | GEO
Project description:5′ methylation of cytosines in DNA molecules is an important epigenetic mark in eukaryotes. Bisulfite sequencing is the gold standard of DNA methylation detection, and whole-genome bisulfite sequencing (WGBS) has been widely used to detect methylation at single-nucleotide resolution on a genome-wide scale. However, sodium bisulfite is known to severely degrade DNA, which, in combination with biases introduced during PCR amplification, leads to unbalanced base representation in the final sequencing libraries. Enzymatic conversion of unmethylated cytosines to uracils can achieve the same end product for sequencing as does bisulfite treatment and does not affect the integrity of the DNA; enzymatic methylation sequencing may, thus, provide advantages over bisulfite sequencing.
2020-10-07 | GSE151616 | GEO
Project description:Genome wide analysis revealed that distal regulatory elements form Low Methylated Regions (LMRs). Even though transcription factor binding is required for LMR formation, we show for the test case CTCF that actual occupancy does not distinguish DNA methylation states. However, in line with a dynamic model of binding and DNA methylation turnover, we find that the product of active demethylation, 5-hydroxymethylcytosine (5hmC), is enriched at LMRs. 5hmC is present at active regulatory regions in stem and somatic cells and as a result a substantial fraction of changes in 5hmC occurs at LMRs. This suggests that transcription factor binding mediates active turnover of DNA methylation as an integral part of reprogramming of regulatory regions. CTCF ChIP bisulfite sequencing in mouse embryonic stem cells and whole genome shotgun bisulfite sequencing of RESTko mouse embryonic stem (ES) cells
2013-12-24 | E-GEOD-39736 | biostudies-arrayexpress