scholarly journals Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy

GigaScience ◽  
2020 ◽  
Vol 9 (5) ◽  
Author(s):  
Katarzyna Murat ◽  
Björn Grüning ◽  
Paulina Wiktoria Poterlowicz ◽  
Gillian Westgate ◽  
Desmond J Tobin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Association Studies ◽  
Methylation Analysis ◽  
Web Based ◽  
Array Platform ◽  
Complex Evaluation ◽  
The Galaxy ◽  
Population Epigenetics ◽  
Analyse Data ◽  
Dna Methylation Analysis

Abstract Background Infinium Human Methylation BeadChip is an array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome based on Illumina’s bead technology and is one of the most common techniques used in epigenome-wide association studies. Finding associations between epigenetic variation and phenotype is a significant challenge in biomedical research. The newest version, HumanMethylationEPIC, quantifies the DNA methylation level of 850,000 CpG sites, while the previous versions, HumanMethylation450 and HumanMethylation27, measured >450,000 and 27,000 loci, respectively. Although a number of bioinformatics tools have been developed to analyse this assay, they require some programming skills and experience in order to be usable. Results We have developed a pipeline for the Galaxy platform for those without experience aimed at DNA methylation analysis using the Infinium Human Methylation BeadChip. Our tool is integrated into Galaxy (http://galaxyproject.org), a web-based platform. This allows users to analyse data from the Infinium Human Methylation BeadChip in the easiest possible way. Conclusions The pipeline provides a group of integrated analytical methods wrapped into an easy-to-use interface. Our tool is available from the Galaxy ToolShed, GitHub repository, and also as a Docker image. The aim of this project is to make Infinium Human Methylation BeadChip analysis more flexible and accessible to everyone.

scholarly journals EWAS-Galaxy: a tools suite for population epigenetics integrated into Galaxy

2019 ◽  
Author(s):  
Katarzyna Murat ◽  
Björn Grüning ◽  
Paulina Wiktoria Poterlowicz ◽  
Gillian Westgate ◽  
Desmond J Tobin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Association Studies ◽  
Web Based ◽  
Genome Wide ◽  
Methylation Assay ◽  
The Galaxy ◽  
Common Technique ◽  
Analyse Data ◽  
User Friendly ◽  
Dna Methylation Analysis

AbstractBackgroundEpigenome-wide association studies (EWAS) analyse genome-wide activity of epigenetic marks in cohorts of different individuals to find associations between epigenetic variation and phenotype. One of the most common technique used in EWAS studies is the Infinium Methylation Assay, which quantifies the DNA methylation level of over 450k loci. Although a number of bioinformatics tools have been developed to analyse the assay they require some programming skills and experience to use them.ResultsWe have developed a collection of user-friendly tools for the Galaxy platform for those without experience aimed at DNA methylation analysis using the Infinium Methylation Assay. Our tool suite is integrated into Galaxy (http://galaxyproject.org), web based platform. This allows users to analyse data from the Infinium Methylation Assay in the easiest possible way.ConclusionsThe EWAS suite provides a group of integrated tools that combine analytical methods into a range of handy analysis pipelines. Our tool suite is available from the Galaxy test toolshed, GitHub repository and also as a Docker image. The aim of this project is to make EWAS analysis more flexible and accessible to everyone.

scholarly journals EpiMOLAS: an intuitive web-based framework for genome-wide DNA methylation analysis

BMC Genomics ◽  
2020 ◽  
Vol 21 (S3) ◽  
Author(s):  
Sheng-Yao Su ◽  
I-Hsuan Lu ◽  
Wen-Chih Cheng ◽  
Wei-Chun Chung ◽  
Pao-Yang Chen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Analysis ◽  
Web Based ◽  
Genome Wide ◽  
Dna Methylation Analysis

scholarly journals Estimands in epigenome-wide association studies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jochen Kruppa ◽  
Miriam Sieg ◽  
Gesa Richter ◽  
Anne Pohrt
Keyword(s):  
Dna Methylation ◽  
Statistical Analysis ◽  
Biological Effect ◽  
Association Studies ◽  
Theoretical Problem ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Cpg Site ◽  
Genome Wide ◽  
Dna Methylation Analysis

Abstract Background In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both. Results We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results. Conclusions The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

scholarly journals P46.03 DNA Methylation Analysis in Smokers and Non-Smokers (Passive and ex-Smokers)

2021 ◽  
Vol 16 (3) ◽  
pp. S490
Author(s):  
D.M. Aguilar-Beltrán ◽  
A.G. Alcázar-Ramos ◽  
A.L. Vega-Rodríguez ◽  
D.G. García-Gutiérrez ◽  
A.D. Bertadillo-Jilote ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Analysis ◽  
Dna Methylation Analysis

scholarly journals DNA Methylation in Solid Tumors: Functions and Methods of Detection

2021 ◽  
Vol 22 (8) ◽  
pp. 4247
Author(s):  
Andrea Martisova ◽  
Jitka Holcakova ◽  
Nasim Izadi ◽  
Ravery Sebuyoya ◽  
Roman Hrstka ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Solid Tumors ◽  
Epigenetic Modification ◽  
Single Gene ◽  
Restriction Enzymes ◽  
Methylation Analysis ◽  
Cpg Dinucleotides ◽  
Sequencing Technologies ◽  
Genome Level ◽  
Dna Methylation Analysis

DNA methylation, i.e., addition of methyl group to 5′-carbon of cytosine residues in CpG dinucleotides, is an important epigenetic modification regulating gene expression, and thus implied in many cellular processes. Deregulation of DNA methylation is strongly associated with onset of various diseases, including cancer. Here, we review how DNA methylation affects carcinogenesis process and give examples of solid tumors where aberrant DNA methylation is often present. We explain principles of methods developed for DNA methylation analysis at both single gene and whole genome level, based on (i) sodium bisulfite conversion, (ii) methylation-sensitive restriction enzymes, and (iii) interactions of 5-methylcytosine (5mC) with methyl-binding proteins or antibodies against 5mC. In addition to standard methods, we describe recent advances in next generation sequencing technologies applied to DNA methylation analysis, as well as in development of biosensors that represent their cheaper and faster alternatives. Most importantly, we highlight not only advantages, but also disadvantages and challenges of each method.

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