An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategiesReport as inadecuate




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BMC Genomics

, 13:42

First Online: 25 January 2012Received: 12 August 2011Accepted: 25 January 2012

Abstract

BackgroundThe 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.

ResultsWe 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.

ConclusionT-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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2164-13-42 contains supplementary material, which is available to authorized users.

Michiel E Adriaens, Magali Jaillard contributed equally to this work.

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Author: Michiel E Adriaens - Magali Jaillard - Lars MT Eijssen - Claus-Dieter Mayer - Chris TA Evelo

Source: https://link.springer.com/article/10.1186/1471-2164-13-42



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