Dataset Information

3D-DIP-Chip: a microarray-based method to measure genomic DNA damage.

ABSTRACT: Genotoxins cause DNA damage, which can result in genomic instability. The genetic changes induced have far-reaching consequences, often leading to diseases such as cancer. A wide range of genotoxins exists, including radiations and chemicals found naturally in the environment, and in man-made forms created by human activity across a variety of industries. Genomic technologies offer the possibility of unravelling the mechanisms of genotoxicity, including the repair of genetic damage, enhancing our ability to develop, test and safely use existing and novel materials. We have developed 3D-DIP-Chip, a microarray-based method to measure the prevalence of genomic genotoxin-induced DNA damage. We demonstrate the measurement of both physical and chemical induced DNA damage spectra, integrating the analysis of these with the associated changes in histone acetylation induced in the epigenome. We discuss the application of the method in the context of basic and translational sciences.

SUBMITTER: Powell JR

PROVIDER: S-EPMC4302307 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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3D-DIP-Chip: a microarray-based method to measure genomic DNA damage.

Powell James Rees JR Bennett Mark Richard MR Evans Katie Ellen KE Yu Shirong S Webster Richard Michael RM Waters Raymond R Skinner Nigel N Reed Simon Huw SH

Scientific reports 20150122

Genotoxins cause DNA damage, which can result in genomic instability. The genetic changes induced have far-reaching consequences, often leading to diseases such as cancer. A wide range of genotoxins exists, including radiations and chemicals found naturally in the environment, and in man-made forms created by human activity across a variety of industries. Genomic technologies offer the possibility of unravelling the mechanisms of genotoxicity, including the repair of genetic damage, enhancing ou ...[more]

PMID: 25609656

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Project description:BACKGROUND: The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. RESULTS: We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. CONCLUSION: T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.

Dataset Information

3D-DIP-Chip: a microarray-based method to measure genomic DNA damage.

Publications

3D-DIP-Chip: a microarray-based method to measure genomic DNA damage.

Similar Datasets

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