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BMC Bioinformatics

, 8:455

First Online: 19 November 2007Received: 09 January 2007Accepted: 19 November 2007

Abstract

BackgroundMicroarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge.

ResultsA computing tool ArrayLeaRNA has been developed for gene expression analysis. It implements a Bayesian approach which is based on the Gumbel distribution and uses printed genomic DNA control features for normalization and for estimation of the parameters of the Bayesian model and prior knowledge from predicted operon structure. The method is compared with two other approaches: the classical LOWESS normalization followed by a two fold cut-off criterion and the OpWise method Price, et al. 2006. BMC Bioinformatics. 7, 19, a published Bayesian approach also using predicted operon structure. The three methods were compared on experimental datasets with prior knowledge of gene expression. With ArrayLeaRNA, data normalization is carried out according to the genomic features which reflect the results of equally transcribed genes; also the statistical significance of the difference in expression is based on the variability of the equally transcribed genes. The operon information helps the classification of genes with low confidence measurements.

ArrayLeaRNA is implemented in Visual Basic and freely available as an Excel add-in at http:-www.ifr.ac.uk-safety-ArrayLeaRNA-

ConclusionWe have introduced a novel Bayesian model and demonstrated that it is a robust method for analysing microarray expression profiles. ArrayLeaRNA showed a considerable improvement in data normalization, in the estimation of the experimental variability intrinsic to each hybridization and in the establishment of a clear boundary between non-changing and differentially expressed genes. The method is applicable to data derived from hybridizations of labelled cDNA samples as well as from hybridizations of labelled cDNA with genomic DNA and can be used for the analysis of datasets where differentially regulated genes predominate.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-8-455 contains supplementary material, which is available to authorized users.

Carmen Pin and Mark Reuter contributed equally to this work.

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Autor: Carmen Pin - Mark Reuter

Fuente: https://link.springer.com/







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