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Abstract Background The liver X receptors (LXRs) are oxysterol sensing nuclear receptors with multiple effects on metabolism and immune cells. However, the complete genome-wide cistrome of LXR in cells of human origin has not yet been provided. Results We performed ChIP-seq in phorbol myristate acetate-differentiated THP-1 cells (macrophage-type) after stimulation with the potent synthetic LXR ligand T0901317 (T09). Microarray gene expression analysis was performed in the same cellular model. We identified 1357 genome-wide LXR locations (FDR < 1%), of which 526 were observed after T09 treatment. De novo analysis of LXR binding sequences identified a DR4-type element as the major motif. On mRNA level T09 up-regulated 1258 genes and repressed 455 genes. Our results show that LXR actions are focused on 112 genomic regions that contain up to 11 T09 target genes per region under the control of highly stringent LXR binding sites with individual constellations for each region. We could confirm that LXR controls lipid metabolism and transport and observed a strong association with apoptosis-related functions. Conclusions This first report on genome-wide binding of LXR in a human cell line provides new insights into the transcriptional network of LXR and its target genes with their link to physiological processes, such as apoptosis. The gene expression microarray and sequence data have been submitted collectively to the NCBI Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo under accession number GSE28319.

Abstract Background A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, controlled by distinct regulatory mechanisms. Many of these pathways and the associated regulatory mechanisms might be obscured by a large number of other significant processes and thus not identified as significant by standard gene set enrichment analysis tools. Results We present a novel method called Independent Enrichment Analysis (IEA) and software TAFFEL that eases the task by clustering genes to subgroups using Gene Ontology categories and transcription regulators. IEA indicates transcriptional regulators putatively controlling biological functions in studied condition. Conclusions We demonstrate that the developed method and TAFFEL tool give new insight to the analysis of differentially expressed genes and can generate novel hypotheses. Our comparison to other popular methods showed that the IEA method implemented in TAFFEL can find important biological phenomena, which are not reported by other methods.

Abstract Background The analysis of over-represented functional classes in a list of genes is one of the most essential bioinformatics research topics. Typical examples of such lists are the differentially expressed genes from transcriptional analysis which need to be linked to functional information represented in the Gene Ontology (GO). Despite the importance of this procedure, there is a little work on consistent evaluation of various GO analysis methods. Especially, there is no literature on creating benchmark datasets for GO analysis tools. Results We propose a methodology for the evaluation of GO analysis tools, which consists of creating gene lists with a selected signal level and a selected number of independent over-represented classes. The methodology starts with a real life GO data matrix, and therefore the generated datasets have similar features to real positive datasets. The user can select the signal level for over-representation, the number of independent positive classes in the dataset, and the size of the final gene list. We present the use of the effective number and various normalizations while embedding the signal to a selected class or classes and the use of binary correlation to ensure that the selected signal classes are independent with each other. The usefulness of generated datasets is demonstrated by comparing different GO class ranking and GO clustering methods. Conclusion The presented methods aid the development and evaluation of GO analysis methods as they enable thorough testing with different signal types and different signal levels. As an example, our comparisons reveal clear differences between compared GO clustering and GO de-correlation methods. The implementation is coded in Matlab and is freely available at the dedicated website http://ekhidna.biocenter.helsinki.fi/users/petri/public/POSGODA/POSGODA.html.

Abstract Background One essential step in the massive analysis of transcriptomic profiles is the calculation of the correlation coefficient, a value used to select pairs of genes with similar or inverse transcriptional profiles across a large fraction of the biological conditions examined. Until now, the choice between the two available methods for calculating the coefficient has been dictated mainly by technological considerations. Specifically, in analyses based on double-channel techniques, researchers have been required to use covariation correlation, i.e. the correlation between gene expression changes measured between several pairs of biological conditions, expressed for example as fold-change. In contrast, in analyses of single-channel techniques scientists have been restricted to the use of coexpression correlation, i.e. correlation between gene expression levels. To our knowledge, nobody has ever examined the possible benefits of using covariation instead of coexpression in massive analyses of single channel microarray results. Results We describe here how single-channel techniques can be treated like double-channel techniques and used to generate both gene expression changes and covariation measures. We also present a new method that allows the calculation of both positive and negative correlation coefficients between genes. First, we perform systematic comparisons between two given biological conditions and classify, for each comparison, genes as increased (I), decreased (D), or not changed (N). As a result, the original series of n gene expression level measures assigned to each gene is replaced by an ordered string of n(n-1)/2 symbols, e.g. IDDNNIDID....DNNNNNNID, with the length of the string corresponding to the number of comparisons. In a second step, positive and negative covariation matrices (CVM) are constructed by calculating statistically significant positive or negative correlation scores for any pair of genes by comparing their strings of symbols. Conclusion This new method, applied to four different large data sets, has allowed us to construct distinct covariation matrices with similar properties. We have also developed a technique to translate these covariation networks into graphical 3D representations and found that the local assignation of the probe sets was conserved across the four chip set models used which encompass three different species (humans, mice, and rats). The application of adapted clustering methods succeeded in delineating six conserved functional regions that we characterized using Gene Ontology information.

Abstract Background High throughput methods of the genome era produce vast amounts of data in the form of gene lists. These lists are large and difficult to interpret without advanced computational or bioinformatic tools. Most existing methods analyse a gene list as a single entity although it is comprised of multiple gene groups associated with separate biological functions. Therefore it is imperative to define and visualize gene groups with unique functionality within gene lists. Results In order to analyse the functional heterogeneity within a gene list, we have developed a method that clusters genes to groups with homogenous functionalities. The method uses Non-negative Matrix Factorization (NMF) to create several clustering results with varying numbers of clusters. The obtained clustering results are combined into a simple graphical presentation showing the functional groups over-represented in the analyzed gene list. We demonstrate its performance on two data sets and show results that improve upon existing methods. The comparison also shows that our method creates a more simplified view that aids in discovery of biological themes within the list and discards less informative classes from the results. Conclusion The presented method and associated software are useful for the identification and interpretation of biological functions associated with gene lists and are especially useful for the analysis of large lists.

Abstract Background Microarray technologies are rapidly becoming available for new species including teleost fishes. We constructed a rainbow trout cDNA microarray targeted at the identification of genes which are differentially expressed in response to environmental stressors. This platform included clones from normalized and subtracted libraries and genes selected through functional annotation. Present study focused on time-course comparisons of stress responses in the brain and kidney and the identification of a set of genes which are diagnostic for stress response. Results Fish were stressed with handling and samples were collected 1, 3 and 5 days after the first exposure. Gene expression profiles were analysed in terms of Gene Ontology categories. Stress affected different functional groups of genes in the tissues studied. Mitochondria, extracellular matrix and endopeptidases (especially collagenases) were the major targets in kidney. Stress response in brain was characterized with dramatic temporal alterations. Metal ion binding proteins, glycolytic enzymes and motor proteins were induced transiently, whereas expression of genes involved in stress and immune response, cell proliferation and growth, signal transduction and apoptosis, protein biosynthesis and folding changed in a reciprocal fashion. Despite dramatic difference between tissues and time-points, we were able to identify a group of 48 genes that showed strong correlation of expression profiles (Pearson r > |0.65|) in 35 microarray experiments being regulated by stress. We evaluated performance of the clone sets used for preparation of microarray. Overall, the number of differentially expressed genes was markedly higher in EST than in genes selected through Gene Ontology annotations, however 63% of stress-responsive genes were from this group. Conclusions 1. Stress responses in fish brain and kidney are different in function and time-course. 2. Identification of stress-regulated genes provides the possibility for measuring stress responses in various conditions and further search for the functionally related genes.

Abstract: The signaling cascade of the transcription factor vitamin D receptor (VDR) is triggered by its specific ligand 1α,25-dihydroxyvitamin D3 (1α,25(OH)2D3). In this study we demonstrate that in THP-1 human monocytic leukemia cells 87.4% of the 1034 most prominent genome-wide VDR binding sites co-localize with loci of open chromatin. At 165 of them 1α,25(OH)2D3 strongly increases chromatin accessibility and has at further 217 sites weaker effects. Interestingly, VDR binding sites in 1α,25(OH)2D3-responsive chromatin regions are far more often composed of direct repeats with 3 intervening nucleotides (DR3s) than those in ligand insensitive regions. DR3-containing VDR sites are enriched in the neighborhood of genes that are involved in controling cellular growth, while non-DR3 VDR binding is often found close to genes related to immunity. At the example of six early VDR target genes we show that the slope of their 1α,25(OH)2D3-induced transcription correlates with the basal chromatin accessibility of their major VDR binding regions. However, the chromatin loci controlling these genes are indistinguishable in their VDR association kinetics. Taken together, ligand responsive chromatin loci represent dynamically regulated contact points of VDR with the genome, from where it controls early 1α,25(OH)2D3 target genes. [Copyright &y& Elsevier]