Genome-wide transcriptional profiling of appressorium development by the rice blast fungus Magnaporthe oryzae.

addresses: College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom.notes: PMCID: PMC3276559The rice blast fungus Magnaporthe oryzae is one of the most significant pathogens affecting global food security. To cause rice blast disease the fungus elaborates a specialised infection structure called an appressorium. Here, we report genome wide transcriptional profile analysis of appressorium development using next generation sequencing (NGS). We performed both RNA-Seq and High-Throughput SuperSAGE analysis to compare the utility of these procedures for identifying differential gene expression in M. oryzae. We then analysed global patterns of gene expression during appressorium development. We show evidence for large-scale gene expression changes, highlighting the role of autophagy, lipid metabolism and melanin biosynthesis in appressorium differentiation. We reveal the role of the Pmk1 MAP kinase as a key global regulator of appressorium-associated gene expression. We also provide evidence for differential expression of transporter-encoding gene families and specific high level expression of genes involved in quinate uptake and utilization, consistent with pathogen-mediated perturbation of host metabolism during plant infection. When considered together, these data provide a comprehensive high-resolution analysis of gene expression changes associated with cellular differentiation that will provide a key resource for understanding the biology of rice blast disease

eulerAPE: Drawing Area-proportional 3-Venn Diagrams Using Ellipses

Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis. The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Other methods use polygons to draw accurate diagrams. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. We present eulerAPE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. We describe the drawing method adopted by eulerAPE and we discuss our evaluation of the effectiveness of eulerAPE and ellipses for drawing random 3-set data. We compare eulerAPE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data