COCOA: coordinate covariation analysis of epigenetic heterogeneity

Abstract A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA’s utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available on Bioconductor (http://bioconductor.org/packages/COCOA).

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COCOA: Coordinate covariation analysis of epigenetic heterogeneity

10.1101/2020.07.09.195289 ◽

2020 ◽

Author(s):

John T. Lawson ◽

Jason P. Smith ◽

Stefan Bekiranov ◽

Francine E. Garrett-Bakelman ◽

Nathan C. Sheffield

Keyword(s):

Dna Methylation ◽

Biological Significance ◽

R Package ◽

Epigenetic Variation ◽

Methylation Data ◽

Computational Framework ◽

Link Type ◽

Epigenetic Signal ◽

Omic Data ◽

Covariation Analysis

AbstractA key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. Here, we present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA’s utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available as a Bioconductor R package (http://bioconductor.org/packages/COCOA).

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Genetic and Epigenetic Changes during the Upward Expansion of Deyeuxia angustifolia Kom. in the Alpine Tundra of the Changbai Mountains, China

Plants ◽

10.3390/plants10020291 ◽

2021 ◽

Vol 10 (2) ◽

pp. 291

Author(s):

Biao Ni ◽

Jian You ◽

Jiangnan Li ◽

Yingda Du ◽

Wei Zhao ◽

...

Keyword(s):

Genetic Diversity ◽

Dna Methylation ◽

Genetic Variation ◽

Soil Properties ◽

Changbai Mountains ◽

Epigenetic Variation ◽

Geographical Distance ◽

Mantel Tests ◽

Epigenetic Diversity ◽

Different Populations

Ecological adaptation plays an important role in the process of plant expansion, and genetics and epigenetics are important in the process of plant adaptation. In this study, genetic and epigenetic analyses and soil properties were performed on D. angustifolia of 17 populations, which were selected in the tundra zone on the western slope of the Changbai Mountains. Our results showed that the levels of genetic and epigenetic diversity of D. angustifolia were relatively low, and the main variation occurred among different populations (amplified fragment length polymorphism (AFLP): 95%, methylation sensitive amplification polymorphism (MSAP): 87%). In addition, DNA methylation levels varied from 23.36% to 35.70%. Principal component analysis (PCA) results showed that soil properties of different populations were heterogeneous. Correlation analyses showed that soil moisture, pH and total nitrogen were significantly correlated with genetic diversity of D. angustifolia, and soil temperature and pH were closely related to epigenetic diversity. Simple Mantel tests and partial Mantel tests showed that genetic variation significantly correlated with habitat or geographical distance. However, the correlation between epigenetic variation and habitat or geographical distance was not significant. Our results showed that, in the case of low genetic variation and genetic diversity, epigenetic variation and DNA methylation may provide a basis for the adaptation of D. angustifolia.

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EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

Scientific Reports ◽

10.1038/s41598-021-84864-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hanyu Zhang ◽

Ruoyi Cai ◽

James Dai ◽

Wei Sun

Keyword(s):

Dna Methylation ◽

Tumor Cells ◽

T Regulatory Cells ◽

Simulated Data ◽

Cell Types ◽

Computational Method ◽

Methylation Data ◽

Cell Type ◽

A Cell ◽

Type Decomposition

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

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CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events

BMC Bioinformatics ◽

10.1186/s12859-021-04191-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Giovanni Scala ◽

Antonio Federico ◽

Dario Greco

Keyword(s):

Dna Methylation ◽

Motif Discovery ◽

Cpg Methylation ◽

Functional Interpretation ◽

Dna Motifs ◽

Molecular Alterations ◽

Link Type ◽

Dna Motif ◽

Quantitative Manner ◽

Dna Motif Discovery

Abstract Background The investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena. Results Here we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs. Conclusions CpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs.

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