Gene Methylation Data – a New Challenge for Bioinformaticians?

2005 ◽  
Vol 44 (04) ◽  
pp. 516-519 ◽  
Author(s):  
H. M. Müller ◽  
H. Fiegl ◽  
M. Widschwendter ◽  
G. Goebel
Keyword(s):  
Dna Methylation ◽  
Study Design ◽  
Marker Genes ◽  
Methylation Data ◽  
Methylation Analysis ◽  
Gene Methylation ◽  
Research Activity ◽  
Molecular Alterations ◽  
Research Questions ◽  
Dna Methylation Analysis

Summary Objectives: Changes in the status of DNA methylation, known as epigenetic alterations, are among the most common molecular alterations in human neoplasia. For the first time, we reported on the analysis of fecal DNA from patients with CRC to determine the feasibility, sensitivity and specificity of this approach. We want to present basic information about DNA methylation analysis in the context of bioinformatics, the study design and several statistical experiences with gene methylation data. Additionally we outline chances and new research questions in the field of DNA methylation. Methods: We present current approaches to DNA methylation analysis based on one reference study. Its study design and the statistical analysis is reflected in the context of biomarker development. Finally we outline perspectives and research questions for statisticians and bioinformaticians. Results: Identification of at least three genes as potential DNA methylation-based tumor marker genes (SFRP2, SFRP5, PGR). Conclusions: DNA methylation analysis is a rising topic in molecular genetics. Gene methylation will push the extension of biobanks to include new types of genetic data. Study design and statistical methods for the detection of methylation biomarkers must be improved. For the purpose of establishing methylation analysis as a new diagnostic/prognostic tool the adaptation of several approaches has become a challenging field of research activity.

scholarly journals Comprehensive DNA Methylation Analysis Reveals a Common Ten-Gene Methylation Signature in Colorectal Adenomas and Carcinomas

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0133836 ◽  
Author(s):  
Árpád V. Patai ◽  
Gábor Valcz ◽  
Péter Hollósi ◽  
Alexandra Kalmár ◽  
Bálint Péterfia ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Colorectal Adenomas ◽  
Methylation Analysis ◽  
Gene Methylation ◽  
Dna Methylation Analysis

Survival Prediction Acute Myeloid Leukemia (AML) Using a Combination of DNA Methylation Analysis and Gene Expression Profiling.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 761-761
Author(s):  
Mathias Ehrich ◽  
Lars Bullinger ◽  
Mattew R. Nelson ◽  
Konstanze Döhner ◽  
Hartmut Döhner ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Expression Profiling ◽  
Myeloid Leukemia ◽  
Patient Survival ◽  
Survival Prediction ◽  
Methylation Data ◽  
Methylation Analysis ◽  
Methylation Patterns ◽  
Dna Methylation Analysis ◽  
Acute Myeloid

Abstract Acute myeloid leukemia is classified by the presence or absence of recurrent cytogenetic aberrations. In order to improve diagnosis and therapy, more recently new studies have been performed to supplement the current classification with refined molecular information based on gene expression profiling. However, it has been established that expression levels of genes are often largely controlled by the state of cytosine methylation in the adjacent promoter region. Thus we were interested to evaluate the quantitative methylation levels for a previously identified predictive set of genes (Bullinger et al. 2004) using a novel technology based on a unique combination of base specific cleavage of single stranded nucleic acids with MALDI TOF detection. We have employed this new quantitative high throughput DNA methylation analysis technology to analyze 147 promoter regions in a total of 192 individuals. The resulting quantitative methylation data was analyzed using a semi-supervised approach to evaluate the quantitative methylation data as a predictor for patient survival. We used a first set of 96 individuals to train a statistical learning algorithm and a second set of 96 samples to validate the trained algorithm. The analysis revealed quantitative methylation patterns as a reliable predictor for survival (p < 0.001). Subsequently, we combined the methylation based predictive model with the results from the expression based predictor. The combination of both models yielded a superior predictive model for patient survival, which outperformed all clinical and cytogenetic risk stratification in the given sample set. The results of this work revealed a potential significance of DNA methylation in the pathophysiology of AML and suggest that DNA-methylation patterns might be useful molecular markers for patient survival prediction based on the fact that large-scale DNA methylation studies can now be performed with reasonable efforts in a limited amount of time. Therefore, these results lay the groundwork for future research which might ultimately enable individualized therapy based on improved molecular characterization of AML.

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.

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.

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