Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TARSII and CARSII: Two approaches for SNP-independent identification of germline differentially methylated regions in mammals.

ABSTRACT: Identifying germline differentially methylated regions (DMRs) in outbred mammals remains a challenge because of difficulty in obtaining single-nucleotide polymorphisms (SNPs). To overcome this difficulty, we developed two computational approaches, TARSII and CARSII, which allow accurate prediction of germline DMRs from DNA methylomes independent of SNPs. Furthermore, we introduce an easy and quick way to validate the predicted germline DMRs with allelic DNA methylation using CGmapTools. Collectively, our strategy can greatly facilitate de novo identification of germline DMRs in outbred mammals. For complete details on the use and execution of this protocol, please refer to Chu et al. (2021).

SUBMITTER: Zhang W

PROVIDER: S-EPMC8931438 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

TARSII and CARSII: Two approaches for SNP-independent identification of germline differentially methylated regions in mammals.

Zhang Wenhao W Zhang Yi Y

STAR protocols 20220315 2

Identifying germline differentially methylated regions (DMRs) in outbred mammals remains a challenge because of difficulty in obtaining single-nucleotide polymorphisms (SNPs). To overcome this difficulty, we developed two computational approaches, TARSII and CARSII, which allow accurate prediction of germline DMRs from DNA methylomes independent of SNPs. Furthermore, we introduce an easy and quick way to validate the predicted germline DMRs with allelic DNA methylation using CGmapTools. Collecti ...[more]

PMID: 35310079

Similar Datasets

ipDMR: identification of differentially methylated regions with interval P-values.

Project description:SummaryipDMR is an R software tool for identification of differentially methylated regions (DMRs) using auto-correlated P-values for individual CpGs from epigenome-wide association analysis using array or bisulfite sequencing data. It summarizes P-values for adjacent CpGs, identifies association peaks and then extends peaks to find boundaries of DMRs. ipDMR uses BED format files as input and is easy to use. Simulations guided by real data found that ipDMR outperformed current available methods and provided slightly higher true positive rates and much lower false discovery rates.Availability and implementationipDMR is available at https://bioconductor.org/packages/release/bioc/html/ENmix.html.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8248314 | biostudies-literature

methylCC: technology-independent estimation of cell type composition using differentially methylated regions.

Project description:A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.

| S-EPMC6883691 | biostudies-literature

De novo identification of differentially methylated regions in the human genome.

Project description:BackgroundThe identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model.ResultsWe show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons.ConclusionsThe agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing. For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data.

| S-EPMC4429355 | biostudies-literature

Identification of differentially methylated regions in new genes associated with knee osteoarthritis.

Project description:Epigenetic changes in articular chondrocytes are associated with osteoarthritis (OA) disease progression. Numerous studies have identified differentially methylated cytosines in OA tissues; however, the consequences of altered CpG methylation at single nucleotides on gene expression and phenotypes are difficult to predict. With the objective of detecting novel genes relevant to OA, we conducted a genome-wide assessment of differentially methylated sites (DMSs) and differentially methylated regions (DMRs). DNA was extracted from visually damaged and normal appearing, non-damaged human knee articular cartilage from the same joint and then subjected to reduced representation bisulfite sequencing. DMRs were identified using a genome-wide systematic bioinformatics approach. A sliding-window of 500 bp was used for screening the genome for regions with clusters of DMSs. Gene expression levels were assessed and cell culture demethylation experiments were performed to further examine top candidate genes associated with damaged articular cartilage. More than 1000 DMRs were detected in damaged osteoarthritic cartilage. Nineteen of these contained five or more DMSs and were located in gene promoters or first introns and exons. Gene expression assessment revealed that hypermethylated DMRs in damaged samples were more consistently associated with gene repression than hypomethylated DMRs were with gene activation. Accordingly, a demethylation agent induced expression of most hypermethylated genes in chondrocytes. Our study revealed the utility of a systematic DMR search as an alternative to focusing on single nucleotide data. In particular, this approach uncovered promising candidates for functional studies such as the hypermethylated protein-coding genes FOXP4 and SHROOM1, which appear to be linked to OA pathology in humans and warrant further investigation.

| S-EPMC4679536 | biostudies-literature

A novel method for identification and quantification of consistently differentially methylated regions.

Project description:Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called "supervised" methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative "unsupervised" approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

| S-EPMC4018258 | biostudies-literature

QDMR: a quantitative method for identification of differentially methylated regions by entropy.

Project description:DNA methylation plays critical roles in transcriptional regulation and chromatin remodeling. Differentially methylated regions (DMRs) have important implications for development, aging and diseases. Therefore, genome-wide mapping of DMRs across various temporal and spatial methylomes is important in revealing the impact of epigenetic modifications on heritable phenotypic variation. We present a quantitative approach, quantitative differentially methylated regions (QDMRs), to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy. QDMR was applied to synthetic methylation patterns and methylation profiles detected by methylated DNA immunoprecipitation microarray (MeDIP-chip) in human tissues/cells. This approach can give a reasonable quantitative measure of methylation difference across multiple samples. Then DMR threshold was determined from methylation probability model. Using this threshold, QDMR identified 10,651 tissue DMRs which are related to the genes enriched for cell differentiation, including 4740 DMRs not identified by the method developed by Rakyan et al. QDMR can also measure the sample specificity of each DMR. Finally, the application to methylation profiles detected by reduced representation bisulphite sequencing (RRBS) in mouse showed the platform-free and species-free nature of QDMR. This approach provides an effective tool for the high-throughput identification of potential functional regions involved in epigenetic regulation.

| S-EPMC3089487 | biostudies-literature

AIMER: A SNP-independent software for identifying imprinting-like allelic methylated regions from DNA methylome.

Project description:Genomic imprinting is essential for mammalian growth and embryogenesis. High-throughput bisulfite sequencing accompanied with parental haplotype-specific information allows analysis of imprinted genes and imprinting control regions (ICRs) on a large scale. Currently, although several allelic methylated regions (AMRs) detection software were developed, methods for detecting imprinted AMRs is still limited. Here, we developed a SNP-independent statistical approach, AIMER, to detect imprinting-like AMRs. By using the mouse frontal cortex methylome as input, we demonstrated that AIMER performs very well in detecting known germline ICRs compared with other methods. Furthermore, we found the putative parental AMRs AIMER detected could be distinguished from sequence-dependent AMRs. Finally, we found a novel germline imprinting-like AMR using WGBS data from 17 distinct mouse tissue samples. The results indicate that AIMER is a good choice for detecting imprinting-like (parent-of-origin-dependent) AMRs. We hope this method will be helpful for future genomic imprinting studies. The Python source code for our project is now publicly available on both GitHub (https://github.com/ZhaoLab-TMU/AIMER) and Gitee (https://gitee.com/zhaolab_tmu/AIMER).

| S-EPMC10809074 | biostudies-literature

Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions.

Project description:Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.

| S-EPMC3973284 | biostudies-literature

Identification of differentially methylated regions in rare diseases from a single-patient perspective.

Project description:BackgroundDNA methylation (5-mC) is being widely recognized as an alternative in the detection of sequence variants in the diagnosis of some rare neurodevelopmental and imprinting disorders. Identification of alterations in DNA methylation plays an important role in the diagnosis and understanding of the etiology of those disorders. Canonical pipelines for the detection of differentially methylated regions (DMRs) usually rely on inter-group (e.g., case versus control) comparisons. However, these tools might perform suboptimally in the context of rare diseases and multilocus imprinting disturbances due to small cohort sizes and inter-patient heterogeneity. Therefore, there is a need to provide a simple but statistically robust pipeline for scientists and clinicians to perform differential methylation analyses at the single patient level as well as to evaluate how parameter fine-tuning may affect differentially methylated region detection.ResultWe implemented an improved statistical method to detect differentially methylated regions in correlated datasets based on the Z-score and empirical Brown aggregation methods from a single-patient perspective. To accurately assess the predictive power of our method, we generated semi-simulated data using a public control population of 521 samples and investigated how the size of the control population, methylation difference, and region size affect DMR detection. In addition, we validated the detection of methylation events in patients suffering from rare multi-locus imprinting disturbance and evaluated how this method could complement existing tools in the context of clinical diagnosis.ConclusionIn this study, we present a robust statistical method to perform differential methylation analysis at the single patient level and describe its optimal parameters to increase DMRs identification performance. Finally, we show its diagnostic utility when applied to rare disorders.

| S-EPMC9758859 | biostudies-literature

Identification of differentially methylated regions associated with both liver fibrosis and hepatocellular carcinoma.

Project description:BackgroundLiver fibrosis is a major risk factor for hepatocellular carcinoma (HCC). We have previously reported that differentially methylated regions (DMRs) are correlated with the fibrosis stages of metabolic dysfunction-associated steatotic liver disease (MASLD). In this study, the methylation levels of those DMRs in liver fibrosis and subsequent HCC were examined.MethodsThe methylation levels of DMRs were investigated using alcoholic cirrhosis and HCC (GSE60753). The data of hepatitis C virus-infected cirrhosis and HCC (GSE60753), and two datasets (GSE56588 and GSE89852) were used for replication analyses. The transcriptional analyses were performed using GSE114564, GSE94660, and GSE142530.ResultsHypomethylated DMR and increased transcriptional level of zinc finger and BTB domain containing 38 (ZBTB38) were observed in HCC. Hypermethylated DMRs, and increased transcriptional levels of forkhead box K1 (FOXK1) and zinc finger CCCH-type containing 3 (ZC3H3) were observed in HCC. The methylation levels of DMR of kazrin, periplakin interacting protein (KAZN) and its expression levels were gradually decreased as cirrhosis progressed to HCC.ConclusionsChanges in the methylation and transcriptional levels of ZBTB38, ZC3H3, FOXK1, and KAZN are important for the development of fibrosis and HCC; and are therefore potential therapeutic targets and diagnostic tools for cirrhosis and HCC.

| S-EPMC10832174 | 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