GenomeRing: alignment visualization based on SuperGenome coordinates.

MOTIVATION: The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge. RESULTS: Here, we present two complementary approaches to tackle this problem. First, we propose the SuperGenome concept for the computation of a common coordinate system for all genomes in a multiple alignment. This coordinate system allows for the consistent placement of genome annotations in the presence of insertions, deletions and rearrangements. Second, we present the GenomeRing visualization that, based on the SuperGenome, creates an interactive overview visualization of the multiple genome alignment in a circular layout. We demonstrate our methods by applying them to an alignment of Campylobacter jejuni strains for the discovery of genomic islands as well as to an alignment of Helicobacter pylori, which we visualize in combination with gene expression data. AVAILABILITY: GenomeRing and example data is available at http://it.inf.uni-tuebingen.de/software/genomering/.

Identifying associations between amino acid changes and meta information in alignments.

MOTIVATION: We present a method that identifies associations between amino acid changes in potentially significant sites in an alignment (taking into account several amino acid properties) with phenotypic data, through the phylogenetic mixed model. The latter accounts for the dependency of the observations (organisms). It is known from previous studies that the pathogenic aspect of many organisms may be associated with a single or just few changes in amino acids, which have a strong structural and/or functional impact on the protein. Discovering these sites is a big step toward understanding pathogenicity. Our method is able to discover such sites in proteins responsible for the pathogenic character of a group of bacteria. RESULTS: We use our method to predict potentially significant sites in the RpoS protein from a set of 209 bacteria. Several sites with significant differences in biological relevant regions were found. AVAILABILITY: Our tool is publicly available on the CRAN network at http://cran.r-project.org/ CONTACT: naya@pasteur.edu.uy SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A technical platform for generating reproducible expression data from Streptomyces coelicolor batch cultivations.

Streptomyces coelicolor, the model species of the genus Streptomyces, presents a complex life cycle of successive morphological and biochemical changes involving the formation of substrate and aerial mycelium, sporulation and the production of antibiotics. The switch from primary to secondary metabolism can be triggered by nutrient starvation and is of particular interest as some of the secondary metabolites produced by related Streptomycetes are commercially relevant. To understand these events on a molecular basis, a reliable technical platform encompassing reproducible fermentation as well as generation of coherent transcriptomic data is required. Here, we investigate the technical basis of a previous study as reported by Nieselt et al. (BMC Genomics 11:10, 2010) in more detail, based on the same samples and focusing on the validation of the custom-designed microarray as well as on the reproducibility of the data generated from biological replicates. We show that the protocols developed result in highly coherent transcriptomic measurements. Furthermore, we use the data to predict chromosomal gene clusters, extending previously known clusters as well as predicting interesting new clusters with consistent functional annotations.

Palladium exposure of barley: uptake and effects.

Motor vehicles are now equipped with exhaust gas catalytic converters containing rare metals, such as palladium (Pd), platinum and rhodium, as catalytic active materials, leading to significantly increased emission of these metals. Compared with platinum and rhodium, low concentrations of Pd have been shown to have more serious effects on cells and organisms. In the present study, uptake of Pd by barley and behaviour of Pd nanoparticles in nutrient solutions used to grow plants were observed in order to develop a model of Pd exposure of plant systems. Pd determination was performed using a selective separation and pre-concentration procedure, which was further developed for this study, and coupled to graphite furnace atomic absorption spectrometry. The results show that uptake of Pd depends on Pd particle diameter. Compared to other toxic metals, like mercury, Pd causes stress effects in leaves at lower concentrations in nutrient solutions. Furthermore, Pd particles are dissolved at different rates, depending on size, in the nutrient solution during plant growth.