Genomic tools to analyse bovine muscle and adipose tissues transcriptomes

摘要

Microarray technology allows us to explore a major subset or almost all genes for an organism. Experimental biases in gene expression profiling occur due to a varied total amount of hybridised mRNA, differing label incorporation rate, spot quantification methodology for image analysis, or bleaching effects of the dye (Ahmed et al., 2004). The accuracy and reproducibility of data generated using microarray technology would be enhanced by the use of a common set of standards as recently proposed (Shi et al, 2006). Although normalization methods have been developed to assess spot quality in an extended examination of spot size, signal-to-noise ratios, background uniformity and the saturation status, the variability introduced by image analysis methodsneeds to be assessed. There is no consensus about what is the best way to analyse microarray data. The aim of this work was to assess different microarray procedures, based on different image analysis and statistic methods, by looking at the variabilityof the results. We have analysed two different data sets from the same experiment by two normalisation methods and two statistical approaches. Utilisation of GenePix/ Madscan (GP/M) or ImaGene/Genesight (IG/G) software influenced the outcome of differentially expressed genes regardless of the statistical method.