scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

scholarly journals scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

2020 ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Regulation Of Gene Expression ◽  
R Package ◽  
Genomic Features ◽  
Distinct Cell ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
Differential Variability

AbstractHigh throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity of DNA methylation and uncover its role in gene regulation. However, limitations of the technology result in sparse CpG coverage, effectively posing challenges to robustly quantify genuine DNA methylation heterogeneity. Here we tackle these issues by introducing scMET, a hierarchical Bayesian model which overcomes data sparsity by sharing information across cells and genomic features, resulting in a robust and biologically interpretable quantification of variability. scMET can be used to both identify highly variable features that drive epigenetic heterogeneity and perform differential methylation and differential variability analysis between pre-specified groups of cells. We demonstrate scMET’s effectiveness on some recent large scale single cell methylation datasets, showing that the scMET feature selection approach facilitates the characterisation of epigenetically distinct cell populations. Moreover, we illustrate how scMET variability estimates enable the formulation of novel biological hypotheses on the epigenetic regulation of gene expression in early development. An R package implementation of scMET is publicly available at https://github.com/andreaskapou/scMET.

scholarly journals DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency

2016 ◽  
Vol 33 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Xiao Wang ◽  
Jinghua Gu ◽  
Leena Hilakivi-Clarke ◽  
Robert Clarke ◽  
Jianhua Xuan
Keyword(s):  
Bayesian Model ◽  
Differential Methylation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Local Dependency

scholarly journals Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution

2019 ◽  
Vol 5 (12) ◽  
pp. eaaw3413 ◽  
Author(s):  
Grant E. Duclos ◽  
Vitor H. Teixeira ◽  
Patrick Autissier ◽  
Yaron B. Gesthalter ◽  
Marjan A. Reinders-Luinge ◽  
...  
Keyword(s):  
Single Cell ◽  
Bronchial Epithelium ◽  
Cell Types ◽  
Premalignant Lesions ◽  
Specific Cell ◽  
Cell Hyperplasia ◽  
Bronchial Epithelial ◽  
Distinct Cell ◽  
Complex Landscape

The human bronchial epithelium is composed of multiple distinct cell types that cooperate to defend against environmental insults. While studies have shown that smoking alters bronchial epithelial function and morphology, its precise effects on specific cell types and overall tissue composition are unclear. We used single-cell RNA sequencing to profile bronchial epithelial cells from six never and six current smokers. Unsupervised analyses led to the characterization of a set of toxin metabolism genes that localized to smoker ciliated cells, tissue remodeling associated with a loss of club cells and extensive goblet cell hyperplasia, and a previously unidentified peri-goblet epithelial subpopulation in smokers who expressed a marker of bronchial premalignant lesions. Our data demonstrate that smoke exposure drives a complex landscape of cellular alterations that may prime the human bronchial epithelium for disease.

scholarly journals Identification and in Silico Characterization of Novel and Conserved MicroRNAs in Methyl Jasmonate-Stimulated Scots Pine (Pinus sylvestris L.) Needles

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 384
Author(s):  
Baiba Krivmane ◽  
Ilze Šņepste ◽  
Vilnis Šķipars ◽  
Igor Yakovlev ◽  
Carl Gunnar Fossdal ◽  
...  
Keyword(s):  
Methyl Jasmonate ◽  
Scots Pine ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Stem Loop ◽  
Protein Coding ◽  
Stem Loop Structure ◽  
In Silico Characterization ◽  
Potential Precursor

MicroRNAs (miRNAs) are non-protein coding RNAs of ~20–24 nucleotides in length that play an important role in many biological and metabolic processes, including the regulation of gene expression, plant growth and developmental processes, as well as responses to stress and pathogens. The aim of this study was to identify and characterize novel and conserved microRNAs expressed in methyl jasmonate-treated Scots pine needles. In addition, potential precursor sequences and target genes of the identified miRNAs were determined by alignment to the Pinus unigene set. Potential precursor sequences were identified using the miRAtool, conserved miRNA precursors were also tested for the ability to form the required stem-loop structure, and the minimal folding free energy indexes were calculated. By comparison with miRBase, 4975 annotated sequences were identified and assigned to 173 miRNA groups, belonging to a total of 60 conserved miRNA families. A total of 1029 potential novel miRNAs, grouped into 34 families were found, and 46 predicted precursor sequences were identified. A total of 136 potential target genes targeted by 28 families were identified. The majority of previously reported highly conserved plant miRNAs were identified in this study, as well as some conserved miRNAs previously reported to be monocot specific. No conserved dicot-specific miRNAs were identified. A number of potential gymnosperm or conifer specific miRNAs were found, shared among a range of conifer species.

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