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Owing to the features of DNA methylation, there is a lack of consensus regarding the optimal approach for normalisation of methylation data.
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Microarray and NGS studies have used different techniques for normalisation; either normalising against total miRNA or using approaches such as scaling and quantile normalisation.
Different statistical approaches (GeNorm, NormFinder) have been developed to identify and validate reference genes appropriate for normalisation in qRT-PCR assays (Andersen et al, 2004; Haller et al, 2004; Vandesompele et al, 2002).
Deep sequencing is an alternative approach for measuring miRNA expression which has the advantage of providing a digital measure that does not require an internal standard for normalisation.
Japan's desire for "normalisation" aims only to build on those benign influences.
Many of those who call for "normalisation" argue that Germany must assert its interests.
For normalisation, two reference genes were used and normalisation factor was calculated by geNorm software (Vandesompele et al. 2002).
Messenger RNA from the housekeeping gene GAPDH was used for normalisation of the cytokine threshold cycle.
Thus, RNM provides an effective method for normalisation of gene expression data obtained from DNA microarrays.
MathCML contains prototypes for normalisation, validation and differentiation of mathematical expressions.
RPL and TUA genes were used as internal controls for normalisation.
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