Fungal phytopathogens continue to cause major economic impact, either directly, through crop losses, or due to the costs of fungicide application. Attempts to understand these organisms are hampered by a lack of fungal genome sequence data. A need exists, however, to develop specific bioinformatics tools to collate and analyse the sequence data that currently is available. A web-accessible gene discovery database (http://cogeme.ex.ac.uk/biosynthesis.html) was developed as a demonstration tool for the analysis of metabolic and signal transduction pathways in pathogenic fungi using incomplete gene inventories. Using Bayesian probability to analyse the currently available gene information from pathogenic fungi, we provide evidence that the obligate pathogen Blumeria graminis possesses all amino acid biosynthetic pathways found in free-living fungi, such as Saccharomyces cerevisiae . Phylogenetic analysis was also used to deduce a gene history of succinate-semialdehyde dehydrogenase, an enzyme in the glutamate and lysine biosynthesis pathways. The database provides a tool and methodology to researchers to direct experimentation towards predicting pathway conservation in pathogenic microorganisms.

We have used DNA microarray technology and 2-D gel electrophoresis combined with mass spectrometry to investigate the effects of a drastic heat shock from 30℃ to 50℃ on a genome-wide scale. This experimental condition is used to differentiate between wild-type cells and those with a constitutively active cAMP-dependent pathway in Saccharomyces cerevisiae . Whilst more than 50% of the former survive this shock, almost all of the latter lose viability. We compared the transcriptomes of the wildtype and a mutant strain deleted for the gene PDE2 , encoding the high-affinity cAMP phosphodiesterase before and after heat shock treatment. We also compared the two heat-shocked samples with one another, allowing us to determine the changes that occur in the pde2 Δ mutant which cause such a dramatic loss of viability after heat shock. Several genes involved in ergosterol biosynthesis and carbon source utilization had altered expression levels, suggesting that these processes might be potential factors in heat shock survival. These predictions and also the effect of the different phases of the cell cycle were confirmed by biochemical and phenotypic analyses. 146 genes of previously unknown function were identified amongst the genes with altered expression levels and deletion mutants in 13 of these genes were found to be highly sensitive to heat shock. Differences in response to heat shock were also observed at the level of the proteome, with a higher level of protein degradation in the mutant, as revealed by comparing 2-D gels of wild-type and mutant heat-shocked samples and mass spectrometry analysis of the differentially produced proteins.

The applications of functional genomics, proteomics and informatics to cancer research have yielded a tremendous amount of information, which is growing all the time. Much of this information is available publicly on the Internet and ranges from general information about different cancers from a patient or clinical viewpoint, through to databases suitable for cancer researchers of all backgrounds, to very specific sites dedicated to individual genes or molecules. A simple search for ‘cancer’ from a typical Web browser search engine yields more than half a million hits; an even more specific search for ‘leukaemia’ (>40 000 hits) or ‘p53’ (>5700 hits) yields far too many hits to allow one to identify particular sites of interest. This review aims to provide a brief guide to some of the resources and databases that can be used as springboards to home in rapidly on information relevant to many fields of cancer research. As such, this article will not focus on a single website but hopes to illustrate some of the ways that postgenomic biology is revolutionizing cancer research. It will cover genomics and proteomics approaches that have been applied to studying global expression patterns in cancers, in addition to providing links ranging from general information about cancer to specific cancer gene mutation databases.

The central dogma of molecular biology has provided a meaningful principle for data integration in the field of genomics. In this context, integration reflects the known transitions from a chromosome to a protein sequence: transcription, intron splicing, exon assembly and translation. There is no such clear principle for integrating proteomics data, since the laws governing protein folding and interactivity are not quite understood. In our effort to bring together independent pieces of information relative to proteins in a biologically meaningful way, we assess the bias of bioinformatics resources and consequent approximations in the framework of small-scale studies. We analyse proteomics data while following both a data-driven (focus on proteins smaller than 10 kDa) and a hypothesis-driven (focus on whole bacterial proteomes) approach. These applications are potentially the source of specialized complements to classical biological ontologies.

Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC). The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC.

Comparative genomics, analyzing variation among individual genomes, is an area of intense investigation. DNA sequencing is usually employed to look for polymorphisms and mutations. Pyrosequencing, a real-time DNA sequencing method, is emerging as a popular platform for comparative genomics. Here we review the use of this technology for mutation scanning, polymorphism discovery and chemical haplotyping. We describe the methodology and accuracy of this technique and discuss how to reduce the cost for large-scale analysis.