Although humans and their closest evolutionary relatives, the chimpanzees, are 98.7% identical in their genomic DNA sequences, they differ in many morphological, behavioral, and cognitive aspects. The underlying genetic basis of many of these differences may be altered gene expression. We have compared the transcriptome in blood leukocytes, liver, and brain of humans, chimpanzees, orangutans, and macaques using microarrays, as well as protein expression patterns of humans and chimpanzees using two-dimensional gel electrophoresis. We also studied three mouse species that are approximately as related to each other as are humans, chimpanzees, and orangutans. We identified species-specific gene expression patterns indicating that changes in protein and gene expression have been particularly pronounced in the human brain.

We have analyzed gene expression in various brain regions of humans and chimpanzees. Within both human and chimpanzee individuals, the transcriptomes of the cerebral cortex are very similar to each other and differ more between individuals than among regions within an individual. In contrast, the transcriptomes of the cerebral cortex, the caudate nucleus, and the cerebellum differ substantially from each other. Between humans and chimpanzees, 10% of genes differ in their expression in at least one region of the brain. The majority of these expression differences are shared among all brain regions. Whereas genes encoding proteins involved in signal transduction and cell differentiation differ significantly between brain regions within individuals, no such pattern is seen between the species. However, a subset of genes that show expression differences between humans and chimpanzees are distributed nonrandomly across the genome. Furthermore, genes that show an elevated expression level in humans are statistically significantly enriched in regions that are recently duplicated in humans.

Current understanding of the phylogeny of prokaryotes is based on the comparison of the highly conserved small ssu-rRNA subunit and similar regions. Although such molecules have proved to be very useful phylogenetic markers, mutational saturation is a problem, due to their restricted lengths. Now, a growing number of complete prokaryotic genomes are available. This paper addresses the problem of determining a prokaryotic phylogeny utilizing the comparison of complete genomes. We introduce a new strategy, GBDP, 'genome blast distance phylogeny', and show that different variants of this approach robustly produce phylogenies that are biologically sound, when applied to 91 prokaryotic genomes. In this approach, first Blast is used to compare genomes, then a distance matrix is computed, and finally a tree- or network-reconstruction method such as UPGMA, Neighbor-Joining, BioNJ or Neighbor-Net is applied.

BackgroundDNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.ResultsWe have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.ConclusionsWe present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org.