MOTIVATION: The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources. RESULTS: We present a nucleosome pattern search method that converts levels of nucleosomal occupancy to a sequence-like format to which BWT searches can be applied. The method is embedded in a nucleosome map browser, 'Nucleosee', an interactive visual tool specifically designed to enhance BWT searches, giving them context and making them suitable for visual discourse analysis of the results. The proposed method is fast, flexible and sufficiently generic for the exploration of data in a broad and interactive way. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm and visual browser are available for testing at http://cpg3.der.usal.es/nucleosee. The source code and installation packages are also available at https://github.com/rodrigoSantamaria/nucleosee. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

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.