Statistical methods for detecting differentially methylated regions based on MethylCap-seq data

Ayyala, Deepak Nag; Frankhouser, David; Ganbat, Javkhlan Ochir; Marcucci, Guido; Bundschuh, Ralf; Yan, Pearlly S.; Lin, Shili

doi:10.1093/bib/bbv089

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…Using the Fisher’s exact test or a Score test could potentially lead to high false positive rates ( 33–35 ). Since a good algorithm to call DMRs should be able to discriminate the natural epigenetic variation at level of single cytosine (biological noise) from a consistent stretch of DNA differentially methylated, we generated a scrambled methylation dataset for estimation of false positive call, randomly swapping the methylation values between all cytosines for each methylation context.…”

Section: Resultsmentioning

confidence: 99%

DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

Catoni

Tsang

Greco

et al. 2018

Nucleic Acids Research

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DNA methylation has been associated with transcriptional repression and detection of differential methylation is important in understanding the underlying causes of differential gene expression. Bisulfite-converted genomic DNA sequencing is the current gold standard in the field for building genome-wide maps at a base pair resolution of DNA methylation. Here we systematically investigate the underlying features of detecting differential DNA methylation in CpG and non-CpG contexts, considering both the case of mammalian systems and plants. In particular, we introduce DMRcaller, a highly efficient R/Bioconductor package, which implements several methods to detect differentially methylated regions (DMRs) between two samples. Most importantly, we show that different algorithms are required to compute DMRs and the most appropriate algorithm in each case depends on the sequence context and levels of methylation. Furthermore, we show that DMRcaller outperforms other available packages and we propose a new method to select the parameters for this tool and for other available tools. DMRcaller is a comprehensive tool for differential methylation analysis which displays high sensitivity and specificity for the detection of DMRs and performs entire genome wide analysis within a few hours.

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Section: Resultsmentioning

confidence: 99%

DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

Catoni

Tsang

Greco

et al. 2018

Nucleic Acids Research

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show abstract

“…Skewness and kurtosis of predictors x 1 to x 4 were (4.75, 1.43, 0.68, 0.48) and (57.60, 3.85, 0.84, 0.41), respectively. Random values from a multivariate normal distribution were generated using the mvnorm procedure in the MASS package (Venables and Ripley 2002) in R; random values from a lognormal distribution were generated using the mvlognormal function in the MethylCapSig package (Ayyala et al 2016).…”

Section: Independent Variablesmentioning

confidence: 99%

Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$ in Multiply Imputed Data

Ginkel

2020

Psychometrika

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Whenever statistical analyses are applied to multiply imputed datasets, specific formulas are needed to combine the results into one overall analysis, also called combination rules. In the context of regression analysis, combination rules for the unstandardized regression coefficients, the t-tests of the regression coefficients, and the F-tests for testing R 2 for significance have long been established. However, there is still no general agreement on how to combine the point estimators of R 2 in multiple regression applied to multiply imputed datasets. Additionally, no combination rules for standardized regression coefficients and their confidence intervals seem to have been developed at all. In the current article, two sets of combination rules for the standardized regression coefficients and their confidence intervals are proposed, and their statistical properties are discussed. Additionally, two improved point estimators of R 2 in multiply imputed data are proposed, which in their computation use the pooled standardized regression coefficients. Simulations show that the proposed pooled standardized coefficients produce only small bias and that their 95% confidence intervals produce coverage close to the theoretical 95%. Furthermore, the simulations show that the newly proposed pooled estimates for R 2 are less biased than two earlier proposed pooled estimates.

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“…Among all options, Bonferroni and false discovery rate (FDR) are the most commonly used. Comprehensive evaluation of almost all tools and statistical methods for identifying DMRs for DNA methylation sequencing data has been summarized [126,145,146,147,148,149]. After the calling of DMRs, the regions of interest often need to be integrated with genome annotation datasets, which allows for determining whether the DMRs are related to genes and gene regulatory regions.…”

Section: Bioinformatics Analysis Of Sequencing-based Dna Methylatimentioning

confidence: 99%

Cell-Free DNA Methylation Profiling Analysis—Technologies and Bioinformatics

Huang

Wang

2019

Cancers

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Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence. Studies have shown that cell-free DNA (cfDNA) has great potential in characterizing tumor status and heterogeneity, as well as the response to therapy and tumor recurrence. DNA methylation is an epigenetic modification that plays an important role in a broad range of biological processes and diseases. It is well known that aberrant DNA methylation is generalizable across various samples and occurs early during the pathogenesis of cancer. Methylation patterns of cfDNA are also consistent with their originated cells or tissues. Systemic analysis of cfDNA methylation profiles has emerged as a promising approach for cancer detection and origin determination. In this review, we will summarize the technologies for DNA methylation analysis and discuss their feasibility for liquid biopsy applications. We will also provide a brief overview of the bioinformatic approaches for analysis of DNA methylation sequencing data. Overall, this review provides informative guidance for the selection of experimental and computational methods in cfDNA methylation-based studies.

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Statistical methods for detecting differentially methylated regions based on MethylCap-seq data

Cited by 13 publications

References 23 publications

DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$ in Multiply Imputed Data

Cell-Free DNA Methylation Profiling Analysis—Technologies and Bioinformatics

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