BackgroundThe development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems'-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells.ResultsThe only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques.ConclusionsBABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets.

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BackgroundThe health benefits of polyunsaturated fatty acids (PUFAs), particularly those of the n-3 series are well documented. The mechanisms by which these effects are mediated are not fully clarified.MethodsWe used microarrays to assess the effects on gene expression in HT29 colon adenocarcinoma cells of exposure to the n-3 fatty acid eicosapentaenoic acid (EPA). HT29 cells were cultured with EPA (150 μM) for up to 24 hr prior to harvesting and isolation of RNA. Microarray results were analyzed within the statistical package 'R', and GeneGo MetaCore was used to identify key pathways of altered gene expression.ResultsEphB4, Vav2 and EphA1 gene expression were identified as significantly altered by EPA treatment. Statistically significant changes in gene expression after HT29 exposure to EPA were confirmed in a second experiment by real-time RT-PCR (TaqMan), This experiment also compared the effects of exposure to EPA to arachadonic acid (AA, n-6). Corresponding changes in protein expression were also assessed by Western blotting.ConclusionsEph receptor mediated signaling is an entirely novel signaling pathway through which EPA may promote a wide range of health benefits, in particular in relation to reduction of colorectal cancer progression.