Conventional and Nanotechniques for DNA Methylation Profiling

Shanmuganathan, Rajasree; Basheer, Nazeema Banu; Amirthalingam, Laxmi; Muthukumar, Harshiny; Kaliaperumal, Rajendran; Shanmugam, Kumaran

doi:10.1016/j.jmoldx.2012.06.007

Cited by 49 publications

(28 citation statements)

References 88 publications

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“…Given the study's focus on clinically applicable assays, we did not include emerging technologies that have not yet been shown to be practically useful in large-scale studies, e.g., nanopores, nanowire transistors, quantum dots, single-molecule real-time sequencing and atomic force spectroscopy 57 . We also did not include genome-wide assays such as whole-genome bisulfite sequencing, reduced-representation bisulfite sequencing, methylated DNA immunoprecipitation sequencing or methyl-CpG binding domain enriched sequencing, given that these assays have been benchmarked previously [28][29][30] , and are currently too cumbersome and expensive for routine clinical diagnostics.…”

Section: Study Design and Assay Selectionmentioning

confidence: 99%

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

Bock¹,

Halbritter²,

Carmona³

et al. 2016

Nat Biotechnol

278

124

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DNA methylation patterns are altered in numerous diseases and often correlate with clinically relevant information such as disease subtypes, prognosis and drug response. With suitable assays and after validation in large cohorts, such associations can be exploited for clinical diagnostics and personalized treatment decisions. Here we describe the results of a community-wide benchmarking study comparing the performance of all widely used methods for DNA methylation analysis that are compatible with routine clinical use. We shipped 32 reference samples to 18 laboratories in seven different countries. Researchers in those laboratories collectively contributed 21 locus-specific assays for an average of 27 predefined genomic regions, as well as six global assays. We evaluated assay sensitivity on low-input samples and assessed the assays' ability to discriminate between cell types. Good agreement was observed across all tested methods, with amplicon bisulfite sequencing and bisulfite pyrosequencing showing the best all-round performance. Our technology comparison can inform the selection, optimization and use of DNA methylation assays in large-scale validation studies, biomarker development and clinical diagnostics.

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Section: Study Design and Assay Selectionmentioning

confidence: 99%

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

Bock¹,

Halbritter²,

Carmona³

et al. 2016

Nat Biotechnol

278

124

View full text Add to dashboard Cite

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“…We speculate that, in the future, the integrated efforts of “omics” research, physical sciences, systems biology, 2, 221 and nanotechnologies 222 will help characterize complex molecular patterns (epigenomic, proteomic, metabolomic, etc.) of host interactions with exogenous factors, giving rise to markers capable of indicating exposure to etiologic fields (Table 2).…”

Section: Advantages and Implications Of The “Etiologic Field Effect” mentioning

confidence: 99%

“…222, 225, 226 Indeed, nano-cytology and nano-cytoarchitecture have been proposed as screening targets for field carcinogenesis. 226, 227 It is therefore conceivable that the sophistication of future biosensors will enable accurate assessment of in-vivo real-time changes in the microenvironmental interactome with high resolution (i.e., in-vivo molecular pathology).…”

Section: Advantages and Implications Of The “Etiologic Field Effect” mentioning

confidence: 99%

Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression

et al. 2015

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The term “field effect” (also known as field defect, field cancerization, or field carcinogenesis) has been used to describe a field of cellular and molecular alteration, which predisposes to the development of neoplasms within that territory. We explore an expanded, integrative concept, “etiologic field effect”, which asserts that various etiologic factors (the exposome including dietary, lifestyle, environmental, microbial, hormonal, and genetic factors) and their interactions (the interactome) contribute to a tissue microenvironmental milieu that constitutes a “field of susceptibility” to neoplasia initiation, evolution, and progression. Importantly, etiological fields predate the acquisition of molecular aberrations commonly considered to indicate presence of filed effect. Inspired by molecular pathological epidemiology (MPE) research, which examines the influence of etiologic factors on cellular and molecular alterations during disease course, an etiologically-focused approach to field effect can: 1) broaden the horizons of our inquiry into cancer susceptibility and progression at molecular, cellular, and environmental levels, during all stages of tumor evolution; 2) embrace host-environment-tumor interactions (including gene-environment interactions) occurring in the tumor microenvironment; and, 3) help explain intriguing observations, such as shared molecular features between bilateral primary breast carcinomas, and between synchronous colorectal cancers, where similar molecular changes are absent from intervening normal colon. MPE research has identified a number of endogenous and environmental exposures which can influence not only molecular signatures in the genome, epigenome, transcriptome, proteome, metabolome and interactome, but also host immunity and tumor behavior. We anticipate that future technological advances will allow the development of in vivo biosensors capable of detecting and quantifying “etiologic field effect” as abnormal network pathology patterns of cellular and microenvironmental responses to endogenous and exogenous exposures. Through an “etiologic field effect” paradigm, and holistic systems pathology (systems biology) approaches to cancer biology, we can improve personalized prevention and treatment strategies for precision medicine.

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“…DNA methylation has also been implicated in the development of cancer [4][5][6] and other diseases [7][8][9] . Furthermore, several studies indicated that the DNA methylation levels could vary by age 10 , sex 11 , disease affected status [4][5][6][7][8][9] methylation-specific PCR, microarray analysis, etc 14 . The high-throughput methods can be classified into two major categories: next-generation sequencing (NGS) and microarrays.…”

Section: Introductionmentioning

confidence: 99%

Positional effects revealed in Illumina Methylation Array and the impact on analysis

Jiao

Zhang

Dai

et al. 2017

Preprint

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Positional effects revealed in Illumina Methylation Array and the impact on analysisWith the evolution of rapid epigenetic research, Illumina Infinium HumanMethylation BeadChips have been widely used to study DNA methylation. However, in evaluating the accuracy of this method, we found that the commonly used Illumina HumanMethylation BeadChips are substantially affected by positional effects; the DNA sample's location in a chip affects the measured methylation levels. We analyzed three HumanMethylation450 and three HumanMethylation27 datasets by using four methods to prove the existence of positional effects. Three datasets were analyzed further for technical replicate analysis or differential methylation CpG sites analysis. The pre-and postcorrection comparisons indicate that the positional effects could alter the measured methylation values and downstream analysis results. Nevertheless, ComBat, linear regression and functional normalization could all be used to minimize such artifact. We recommend performing ComBat to correct positional effects followed by the correction of batch effects in data preprocessing as this procedure slightly outperforms the others. In addition, randomizing the sample placement should be a critical laboratory practice for using such experimental platforms. Code for our method is freely available at:https://github.com/ChuanJ/posibatch. . DNA methylation has also been implicated in the development of cancer 4-6 and other diseases [7][8][9] . Furthermore, several studies indicated that the DNA methylation levels could vary by age 10 , sex 11, disease affected status [4][5][6][7][8][9] , circadian rhythms 12 , tissues types 13 and other factors.Many methods have been used to measure the methylation levels of cytosines, such as blotting, atomic force spectroscopy, genomic sequencing, bisulfite sequencing, peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/153858 doi: bioRxiv preprint first posted online . It has twelve sample sections in one array arranged in a six by two format (Fig.S1). While Methyl27 measures the methylation status of over 27,000 CpG sites in the genome using the Type I assay with twelve sample locations arranged by twelve rows (Fig.S1) peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/153858 doi: bioRxiv preprint first posted online In this article, we compared three methods for correcting the positional effects:ComBat, linear regression model and functional normalization. ComBat adjusts for known batches using an empirical Bayesian method even in small sample sizes, the linear regression model is a classical method to remove known confounders, and FN is an unsupervised method using control probes as surrogates for unwanted variation Few studies had properly addressed the positional effects, which could lead to p...

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Conventional and Nanotechniques for DNA Methylation Profiling

Cited by 49 publications

References 88 publications

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

Quantitative comparison of DNA methylation assays for biomarker development and clinical applications

Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression

Positional effects revealed in Illumina Methylation Array and the impact on analysis

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