Nucleosomes contribute to compacting the genome into the nucleus and regulate the physical access of regulatory proteins to DNA either directly or through the epigenetic modifications of the histone tails. Precise mapping of nucleosome positioning across the genome is, therefore, essential to understanding the genome regulation. In recent years, several experimental protocols have been developed for this purpose that include the enzymatic digestion, chemical cleavage or immunoprecipitation of chromatin followed by next-generation sequencing of the resulting DNA fragments. Here, we compare the performance and resolution of these methods from the initial biochemical steps through the alignment of the millions of short-sequence reads to a reference genome to the final computational analysis to generate genome-wide maps of nucleosome occupancy. Because of the lack of a unified protocol to process data sets obtained through the different approaches, we have developed a new computational tool (NUCwave), which facilitates their analysis, comparison and assessment and will enable researchers to choose the most suitable method for any particular purpose. NUCwave is freely available at http://nucleosome.usal.es/nucwave along with a step-by-step protocol for its use.

MOTIVATION: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data. RESULTS: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. AVAILABILITY AND IMPLEMENTATION: BicOverlapper 2.0 is available at: http://vis.usal.es/bicoverlapper2

BACKGROUND: DNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol to exploit the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts. RESULTS: We extensively filtered probes present in Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to generate custom Chip Description Files (CDF) in order to compare their efficiency. We experimentally tested the potential of our approach by measuring the differential expression of 4904 genes in the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress. The results showed a Pearson correlation coefficient of 0.943 between both platforms, indicating that TMAs are as reliable as EMAs for quantitative expression analysis. A significant advantage of TMAs over EMAs is the possibility of detecting non-annotated transcripts generated only under specific physiological conditions. To take full advantage of this property, we have used a target-labelling protocol that preserves the original polarity of the transcripts and, therefore, allows the strand-specific differential expression of non-annotated transcripts to be determined. By using a segmentation algorithm prior to generating the corresponding custom CDFs, we identified and quantitatively measured the expression of 510 transcripts longer than 180 nucleotides and not overlapping previously annotated ORFs that were differentially expressed at least 2-fold under oxidative stress. CONCLUSIONS: We show that the information derived from TMA hybridization can be processed simultaneously for high-resolution qualitative and quantitative analysis of the differential expression of well-characterized genes and of previously non-annotated and antisense transcripts. The consistency of the performance of TMA, their genome-wide coverage and adaptability to updated genome annotations, and the possibility of measuring strand-specific differential expression makes them a tool of choice for the analysis of gene expression in any organism for which TMA platforms are available.

UNLABELLED: BicOverlapper is a tool to visualize biclusters from gene-expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. AVAILABILITY: The BicOverlapper software and supplementary material are available at http://vis.usal.es/bicoverlapper

BACKGROUND: Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results. RESULTS: We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapper CONCLUSION: The main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually.