scholarly journals MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

2014 ◽  
Author(s):  
Mark D Robinson ◽  
Abdullah Kahraman ◽  
Charity W Law ◽  
Helen Lindsay ◽  
Malgorzata Nowicka ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Statistical Methods ◽  
Empirical Bayes ◽  
Large Scale ◽  
Rapid Development ◽  
Methylation Status ◽  
Regulation Of Gene Expression ◽  
Data Types ◽  
Cpg Dinucleotides ◽  
Reversible Addition

DNA methylation, and specifically the reversible addition of methyl groups at CpG dinucleotides genome-wide, represents an important layer that is associated with the regulation of gene expression. In particular, aberrations in the methylation status have been noted across a diverse set of pathological states, including cancer. With the rapid development and uptake of large scale sequencing of short DNA fragments, there has been an explosion of data analytic methods for processing and discovering changes in DNA methylation across diverse data types. In this mini-review, we aim to condense many of the salient challenges, such as experimental design, statistical methods for differential methylation detection and critical considerations such as cell type composition and the potential confounding that can arise from batch effects, into a compact and accessible format. Our main interests, from a statistical perspective, include the practical use of empirical Bayes or hierarchical models, which have been shown to be immensely powerful and flexible in genomics and the procedures by which control of false discoveries are made. Of course, there are many critical platform-specific data preprocessing aspects that we do not discuss here. In addition, we do not make formal performance comparisons of the methods, but rather describe the commonly used statistical models and many of the pertinent issues; we make some recommendations for further study.

Progressive alteration of DNA methylation of Alu, MGMT, MINT2, and TFPI2 genes in colonic mucosa during colorectal cancer development

2021 ◽  
pp. 1-6
Author(s):  
Ben Kang ◽  
Hyun Seok Lee ◽  
Seong Woo Jeon ◽  
Soo Yeun Park ◽  
Gyu Seog Choi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Healthy Subjects ◽  
Large Scale ◽  
Methylation Status ◽  
Clinical Information ◽  
Field Cancerization ◽  
Aberrant Methylation ◽  
Clinical Value ◽  
Mortality And Morbidity

BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. It is characterized by different pathways of carcinogenesis and is a heterogeneous disease with diverse molecular landscapes that reflect histopathological and clinical information. Changes in the DNA methylation status of colon epithelial cells have been identified as critical components in CRC development and appear to be emerging biomarkers for the early detection and prognosis of CRC. OBJECTIVE: To explore the underlying disease mechanisms and identify more effective biomarkers of CRC. METHODS: We compared the levels and frequencies of DNA methylation in 11 genes (Alu, APC, DAPK, MGMT, MLH1, MINT1, MINT2, MINT3, p16, RGS6, and TFPI2) in colorectal cancer and its precursor adenomatous polyp with normal tissue of healthy subjects using pyrosequencing and then evaluated the clinical value of these genes. RESULTS: Aberrant methylation of Alu, MGMT, MINT2, and TFPI2 genes was progressively accumulated during the normal-adenoma-carcinoma progression. Additionally, CGI methylation occurred either as an adenoma-associated event for APC, MLH1, MINT1, MINT31, p16, and RGS6 or a tumor-associated event for DAPK. Moreover, relatively high levels and frequencies of DAPK, MGMT, and TFPI2 methylation were detected in the peritumoral nonmalignant mucosa of cancer patients in a field-cancerization manner, as compared to normal mucosa from healthy subjects. CONCLUSION: This study identified several biomarkers associated with the initiation and progression of CRC. As novel findings, they may have important clinical implications for CRC diagnostic and prognostic applications. Further large-scale studies are needed to confirm these findings.

scholarly journals Genome-Wide DNA Methylation Pattern of Cancer Stem Cells in Esophageal Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382098379
Author(s):  
Xiying Yu ◽  
Ying Teng ◽  
Xingran Jiang ◽  
Hui Yuan ◽  
Wei Jiang
Keyword(s):  
Dna Methylation ◽  
Stem Cells ◽  
Esophageal Cancer ◽  
Cancer Stem Cells ◽  
Side Population ◽  
Methylation Status ◽  
Methylation Profile ◽  
Cpg Dinucleotides ◽  
Genome Wide ◽  
Differentially Methylated Genes

Background: Cancer stem cells (CSCs) are considered the main cause of cancer recurrence and metastasis, and DNA methylation is involved in the maintenance of CSCs. However, the methylation profile of esophageal CSCs remains unknown. Methods: Side population (SP) cells were isolated from esophageal squamous cell carcinoma (ESCC) cell lines KYSE150 and EC109. Sphere-forming cells were collected from human primary esophageal cancer cells. SP cells and sphere-forming cells were used as substitutes for cancer stem-like cells. We investigated the genome-wide DNA methylation profile in esophageal cancer stem-like cells using reduced representation bisulfite sequencing (RRBS). Results: Methylated cytosine (mC) was found mostly in CpG dinucleotides, located mostly in the intronic, intergenic, and exonic regions. Forty intersected differentially methylated regions (DMRs) were identified in these 3 groups of samples. Thirteen differentially methylated genes with the same alteration trend were detected; these included OTX1, SPACA1, CD163L1, ST8SIA2, TECR, CADM3, GRM1, LRRK1, CHSY1, PROKR2, LINC00658, LOC100506688, and NKD2. DMRs covering ST8SIA2 and GRM1 were located in exons. These differentially methylated genes were involved in 10 categories of biological processes and 3 cell signaling pathways. Conclusions: When compared to non-CSCs, cancer stem-like cells have a differential methylation status, which provides an important biological base for understanding esophageal CSCs and developing therapeutic targets for esophageal cancer.

scholarly journals "Same Difference": Comprehensive evaluation of three DNA methylation measurement platforms

2016 ◽  
Author(s):  
Thadeous J Kacmarczyk ◽  
Mame P. Fall ◽  
Xihui Zhang ◽  
Yuan Xin ◽  
Yushan Li ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Restriction Enzyme ◽  
Comprehensive Evaluation ◽  
Methylation Status ◽  
Regulation Of Gene Expression ◽  
Starting Material ◽  
Reduced Representation ◽  
Targeted Capture ◽  
Areas Of Interest

ABSTRACTBackgroundDNA methylation in CpG context is fundamental to the epigenetic regulation of gene expression in high eukaryotes. Disorganization of methylation status is implicated in many diseases, cellular differentiation, imprinting, and other biological processes. Techniques that enrich for biologically relevant genomic regions with high CpG content are desired, since, depending on the size of an organism’s methylome, the depth of sequencing required to cover all CpGs can be prohibitively expensive. Currently, restriction enzyme based reduced representation bisulfite sequencing and its modified protocols are widely used to study methylation differences. Recently, Agilent Technologies and Roche NimbleGen have ventured to both reduce sequencing costs and capture CpGs of known biological relevance by marketing in-solution custom-capture hybridization platforms. We aimed to evaluate the similarities and differences of these three methods considering each targets approximately 10-13% of the human methylome.ResultsOverall, the regions covered per platform were as expected: targeted capture based methods covered >95% of their designed regions whereas the restriction enzyme-based method covered >70% of the expected fragments. While the total number of CpG loci shared by all methods was low, ~30% of any platform, the methylation levels of CpGs common across platforms were concordant. Annotation of CpG loci with genomic features revealed roughly the same proportions of feature annotations across the three platforms. Targeted capture methods encompass similar amounts of annotations with the restriction enzyme based method covering fewer promoters (~9%) and shores (~8%) and more unannotated loci (7-14%).ConclusionsAlthough all methods are largely consistent in terms of covered CpG loci and cover similar proportions of annotated CpG loci, the restriction based enrichment results in more unannotated regions and the commercially available capture methods result in less off-target regions. Quality of DNA is very important for restriction based enrichment and starting material can be low. Conversely, quality of the starting material is less important for capture methods, and at least twice the amount of starting material is required. Pricing is marginally less for restriction based enrichment, and number of samples to be prepared is not restricted to the number of samples a kit supports. The one advantage of capture libraries is the ability to custom design areas of interest. The choice of the technique should be decided by the number of samples, the quality and quantity of DNA available and the biological areas of interest since comparable data are obtained from all platforms.

scholarly journals scDIOR: single cell RNA-seq data IO software

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Huijian Feng ◽  
Lihui Lin ◽  
Jiekai Chen
Keyword(s):  
Single Cell ◽  
Programming Languages ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Rapid Development ◽  
Data Transformation ◽  
Rna Seq ◽  
Data Types ◽  
User Friendly ◽  
Cell Data

Abstract Background Single-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression. The rapid development of computational methods promotes the insight of heterogeneous single-cell data. An increasing number of tools have been provided for biological analysts, of which two programming languages- R and Python are widely used among researchers. R and Python are complementary, as many methods are implemented specifically in R or Python. However, the different platforms immediately caused the data sharing and transformation problem, especially for Scanpy, Seurat, and SingleCellExperiemnt. Currently, there is no efficient and user-friendly software to perform data transformation of single-cell omics between platforms, which makes users spend unbearable time on data Input and Output (IO), significantly reducing the efficiency of data analysis. Results We developed scDIOR for single-cell data transformation between platforms of R and Python based on Hierarchical Data Format Version 5 (HDF5). We have created a data IO ecosystem between three R packages (Seurat, SingleCellExperiment, Monocle) and a Python package (Scanpy). Importantly, scDIOR accommodates a variety of data types across programming languages and platforms in an ultrafast way, including single-cell RNA-seq and spatial resolved transcriptomics data, using only a few codes in IDE or command line interface. For large scale datasets, users can partially load the needed information, e.g., cell annotation without the gene expression matrices. scDIOR connects the analytical tasks of different platforms, which makes it easy to compare the performance of algorithms between them. Conclusions scDIOR contains two modules, dior in R and diopy in Python. scDIOR is a versatile and user-friendly tool that implements single-cell data transformation between R and Python rapidly and stably. The software is freely accessible at https://github.com/JiekaiLab/scDIOR.

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 Physical Activity and DNA Methylation in Humans

2021 ◽  
Vol 22 (23) ◽  
pp. 12989
Author(s):  
Witold Józef Światowy ◽  
Hanna Drzewiecka ◽  
Michalina Kliber ◽  
Maria Sąsiadek ◽  
Paweł Karpiński ◽  
...  
Keyword(s):  
Gene Expression ◽  
Physical Activity ◽  
Dna Methylation ◽  
Current Knowledge ◽  
Methylation Status ◽  
Cpg Islands ◽  
Great Majority ◽  
Dna Methyltransferases ◽  
Epigenetic Mark ◽  
Cpg Dinucleotides

Physical activity is a strong stimulus influencing the overall physiology of the human body. Exercises lead to biochemical changes in various tissues and exert an impact on gene expression. Exercise-induced changes in gene expression may be mediated by epigenetic modifications, which rearrange the chromatin structure and therefore modulate its accessibility for transcription factors. One of such epigenetic mark is DNA methylation that involves an attachment of a methyl group to the fifth carbon of cytosine residue present in CG dinucleotides (CpG). DNA methylation is catalyzed by a family of DNA methyltransferases. This reversible DNA modification results in the recruitment of proteins containing methyl binding domain and further transcriptional co-repressors leading to the silencing of gene expression. The accumulation of CpG dinucleotides, referred as CpG islands, occurs at the promoter regions in a great majority of human genes. Therefore, changes in DNA methylation profile affect the transcription of multiple genes. A growing body of evidence indicates that exercise training modulates DNA methylation in muscles and adipose tissue. Some of these epigenetic markers were associated with a reduced risk of chronic diseases. This review summarizes the current knowledge about the influence of physical activity on the DNA methylation status in humans.

305 SPERM CHROMATIN STRUCTURE AND DNA METHYLATION BULLS

2006 ◽  
Vol 18 (2) ◽  
pp. 260
Author(s):  
K. Hengstberger ◽  
N. Phutikanit ◽  
O. Berking ◽  
M. D'Occhio ◽  
D. Sester ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Conformational Changes ◽  
Chromatin Structure ◽  
Methylation Status ◽  
Regulation Of Gene Expression ◽  
Bos Indicus ◽  
Methylation Pattern ◽  
Embryonic Mortality ◽  
Sperm Chromatin ◽  
Detergent Solution

Mammalian spermatogenesis is a complex process that involves genome-wide deprogramming and reprogramming of DNA methylation at different stages in order to produce sperm that are capable of fertilization and also TO support ongoing embryo development (Webster et al. 2005 PNAS 102, 4068-4073). Embryonic mortality occurs when there is inappropriate expression of developmentally regulated genes of both maternal and paternal origin. DNA methylation is associated with epigenetic regulation of gene expression and it was recently shown that aberrant DNA methylation causes a change in sperm chromatin structure (Webster et al. 2005). Studies, primarily in man, have indicated that sperm chromatin instability, as determined by the sperm chromatin structure assay (SCSA), does not influence fertilization but is linked with early pregnancy failure (Evenson and Jost 2000 Methods Cell Sci. 22, 169-189). The aim of the present study was to investigate the relationship between chromatin structure and DNA methylation in bull sperm. Chromatin structure was determined using the SCSA that involves the exposure of sperm to a low pH (1.2) detergent solution followed by the addition of acridine orange (AO). When exposed to a 488 nm laser, AO fluoresces green when bound to native (double-stranded) DNA and red when bound to denatured (single-stranded) DNA. The SCSA yields a value for the DNA fragmentation index (DFI), and in men a DFI > 27-30% is associated with early embryonic mortality (Larson-Cook et al. 2003 Fertil. Steril. 80, 895-902). Semen was obtained by electroejaculation from Zebu (Bos indicus) bulls (n = 12) in a subtropical environment; a proportion of bulls had a DFI < 15% (n = 4) and the remainder a DFI > 27% (n = 8). The DNA methylation pattern, as determined by the amplified methylation polymorphism (AMP) protocol (Webster et al. 2005), appeared to differ between sperm with a DFI < 15% and sperm with a DFI > 27% when assessed by principle coordinate analysis. Also, sperm with a DFI < 15% had a more consistent methylation pattern compared with apparent differences in methylation among sperm with a DFI > 27%. These preliminary findings could be interpreted to suggest that methylation status can contribute to chromatin structure in sperm of mature bulls. It remains to be determined whether the environment can influence sperm chromatin status in bulls by altering DNA methylation which then impacts on the conformational changes in DNA that occur during spermiogenesis. This work was supported, in part, by Meat and Livestock Australia.

scholarly journals Whole-blood DNA Methylation Markers for Risk Stratification in Colorectal Cancer Screening: A Systematic Review

Cancers ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 912 ◽  
Author(s):  
Raut ◽  
Guan ◽  
Schrotz-King ◽  
Brenner
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Risk Stratification ◽  
Colorectal Neoplasms ◽  
Whole Blood ◽  
Large Scale ◽  
Methylation Status ◽  
Population Characteristics ◽  
Risk Scores ◽  
Study Population

DNA methylation profiles within whole-blood samples have been reported to be associated with colorectal cancer (CRC) occurrence and might enable risk stratification for CRC. We systematically reviewed and summarized studies addressing the association of whole-blood DNA methylation markers and risk of developing CRC or its precursors. We searched PubMed and ISI Web of Knowledge to identify relevant studies published until 12th November 2018. Two reviewers independently extracted data on study population characteristics, candidate genes, methylation measurement methods, methylation levels of patients in comparison to healthy controls, p-values, and odds ratios of the markers. Overall, 19 studies reporting 102 methylation markers for risk assessment of colorectal neoplasms met our inclusion criteria. The studies mostly used Methylation Specific Polymerase Chain Reaction (MS-PCR) for assessing the methylation status of a defined set of genes. Only two studies applied array-based genome-wide assays to assess the methylation levels. Five studies incorporated panels consisting of 2–10 individual methylation markers to assess their potential for stratifying the risk of developing colorectal neoplasms. However, none of these associations was confirmed in an independent cohort. In conclusion, whole-blood DNA methylation markers may be useful as biomarkers for risk stratification in CRC screening, but reproducible risk prediction algorithms are yet to be established by large scale epigenome-wide studies with thorough validation of results in prospective study cohorts including large screening populations. The possibilities of enhancing predictive power by combining methylation data with polygenetic risk scores and environmental risk factors need to be explored.

scholarly journals Alcohol consumption is associated with widespread changes in blood DNA methylation: analysis of cross-sectional and longitudinal data

2018 ◽  
Author(s):  
Pierre-Antoine Dugué ◽  
Rory Wilson ◽  
Benjamin Lehne ◽  
Harindra Jayasekara ◽  
Xiaochuan Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Alcohol Consumption ◽  
Large Scale ◽  
Alcohol Intake ◽  
Methylation Status ◽  
Repeated Measurements ◽  
Cross Sectional ◽  
Blood Samples ◽  
Melbourne Collaborative Cohort Study ◽  
Risk Of Disease

ABSTRACTBackground:DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart.Methods:Using the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined using baseline peripheral blood samples from 5,606 adult Melbourne Collaborative Cohort Study (MCCS) participants. For a subset of 1,088 of them, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. Independent data from the LOLIPOP (N=4,042) and KORA (N=1,662) cohorts were used to replicate associations discovered in the MCCS.Results:Cross-sectional analyses identified 1,414 CpGs associated with alcohol intake at P<10-7, 1,243 of which had not been reported previously. Of these 1,243 novel associations, 1,078 were replicated (P<0.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated (P<0.05) 403 of 518 associations that had been reported previously. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1,414 CpGs, 530 were differentially methylated (P<0.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1,414 cross-sectional associations.Conclusion:Our study indicates that, for middle-aged and older adults, alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with changes in alcohol consumption.

scholarly journals The role of DNA methylation in ageing and cancer

2018 ◽  
Vol 77 (4) ◽  
pp. 412-422 ◽  
Author(s):  
A. E. Morgan ◽  
T. J. Davies ◽  
M. T. Mc Auley
Keyword(s):  
Dna Methylation ◽  
Gene Silencing ◽  
Methylation Status ◽  
Electrochemical Techniques ◽  
Regulation Of Gene Expression ◽  
Homeobox Gene ◽  
Gene Promoter ◽  
Disease States ◽  
Potential Biomarker ◽  
Aberrant Dna Methylation

The aim of the present review paper is to survey the literature related to DNA methylation, and its association with cancer and ageing. The review will outline the key factors, including diet, which modulate DNA methylation. Our rationale for conducting this review is that ageing and diseases, including cancer, are often accompanied by aberrant DNA methylation, a key epigenetic process, which is crucial to the regulation of gene expression. Significantly, it has been observed that with age and certain disease states, DNA methylation status can become disrupted. For instance, a broad array of cancers are associated with promoter-specific hypermethylation and concomitant gene silencing. This review highlights that hypermethylation, and gene silencing, of the EN1 gene promoter, a crucial homeobox gene, has been detected in various forms of cancer. This has led to this region being proposed as a potential biomarker for diseases such as cancer. We conclude the review by describing a recently developed novel electrochemical method that can be used to quantify the level of methylation within the EN1 promoter and emphasise the growing trend in the use of electrochemical techniques for the detection of aberrant DNA methylation.

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