Philip Zimmermann

GENEVESTIGATOR. Arabidopsis Microarray Database and Analysis Toolbox

High-throughput gene expression analysis has become a frequent and powerful research tool in biology. At present, however, few software applications have been developed for biologists to query large microarray gene expression databases using a Web-browser interface. We present GENEVESTIGATOR, a database and Web-browser data mining interface for Affymetrix GeneChip data. Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs. Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs. Using GENEVESTIGATOR, the gene expression profiles of more than 22,000 Arabidopsis genes can be obtained, including those of 10,600 currently uncharacterized genes. The objective of this software application is to direct gene functional discovery and design of new experiments by providing plant biologists with contextual information on the expression of genes. The database and analysis toolbox is available as a community resource at https://www.genevestigator.ethz.ch.

Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation.

A systematic comparison and evaluation of biclustering methods for gene expression data

MOTIVATION In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. RESULTS First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.

Genome-Wide Analysis of Hydrogen Peroxide-Regulated Gene Expression in Arabidopsis Reveals a High Light-Induced Transcriptional Cluster Involved in Anthocyanin Biosynthesis

In plants, reactive oxygen species and, more particularly, hydrogen peroxide (H2O2) play a dual role as toxic by-products of normal cell metabolism and as regulatory molecules in stress perception and signal transduction. Peroxisomal catalases are an important sink for photorespiratory H2O2. Using ATH1 Affymetrix microarrays, expression profiles were compared between control and catalase-deficient Arabidopsis (Arabidopsis thaliana) plants. Reduced catalase levels already provoked differences in nuclear gene expression under ambient growth conditions, and these effects were amplified by high light exposure in a sun simulator for 3 and 8 h. This genome-wide expression analysis allowed us to reveal the expression characteristics of complete pathways and functional categories during H2O2 stress. In total, 349 transcripts were significantly up-regulated by high light in catalase-deficient plants and 88 were down-regulated. From this data set, H2O2 was inferred to play a key role in the transcriptional up-regulation of small heat shock proteins during high light stress. In addition, several transcription factors and candidate regulatory genes involved in H2O2 transcriptional gene networks were identified. Comparisons with other publicly available transcriptome data sets of abiotically stressed Arabidopsis revealed an important intersection with H2O2-deregulated genes, positioning elevated H2O2 levels as an important signal within abiotic stress-induced gene expression. Finally, analysis of transcriptional changes in a combination of a genetic (catalase deficiency) and an environmental (high light) perturbation identified a transcriptional cluster that was strongly and rapidly induced by high light in control plants, but impaired in catalase-deficient plants. This cluster comprises the complete known anthocyanin regulatory and biosynthetic pathway, together with genes encoding unknown proteins.

Genome-Scale Proteomics Reveals Arabidopsis thaliana Gene Models and Proteome Dynamics

We have assembled a proteome map for Arabidopsis thaliana from high-density, organ-specific proteome catalogs that we generated for different organs, developmental stages, and undifferentiated cultured cells. We matched 86,456 unique peptides to 13,029 proteins and provide expression evidence for 57 gene models that are not represented in the TAIR7 protein database. Analysis of the proteome identified organ-specific biomarkers and allowed us to compile an organ-specific set of proteotypic peptides for 4105 proteins to facilitate targeted quantitative proteomics surveys. Quantitative information for the identified proteins was used to establish correlations between transcript and protein accumulation in different plant organs. The Arabidopsis proteome map provides information about genome activity and proteome assembly and is available as a resource for plant systems biology.

Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.

BicAT: a biclustering analysis toolbox

SUMMARY Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user interface. Furthermore, BicAT provides different facilities for data preparation, inspection and postprocessing such as discretization, filtering of biclusters according to specific criteria or gene pair analysis for constructing gene interconnection graphs. The possibility to use different biclustering algorithms inside a single graphical tool allows the user to compare clustering results and choose the algorithm that best fits a specific biological scenario. The toolbox is described in the context of gene expression analysis, but is also applicable to other types of data, e.g. data from proteomics or synthetic lethal experiments. AVAILABILITY The BicAT toolbox is freely available at http://www.tik.ee.ethz.ch/sop/bicat and runs on all operating systems. The Java source code of the program and a developers guide is provided on the website as well. Therefore, users may modify the program and add further algorithms or extensions.

RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization

BackgroundRT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts.ResultsUsing the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions.ConclusionWe therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://www.genevestigator.com.

Gene Expression Analysis, Proteomics, and Network Discovery

Technological advances in biological experimentation are now enabling researchers to investigate living systems on an unprecedented scale by studying genomes, proteomes, or molecular networks in their entirety. Genomics technologies have led to a paradigm shift in biological experimentation because

Genevestigator Transcriptome Meta-Analysis and Biomarker Search using Rice and Barley Gene Expression Databases

The wide-spread use of microarray technologies to study plant transcriptomes has led to important discoveries and to an accumulation of profiling data covering a wide range of different tissues, developmental stages, perturbations, and genotypes. Querying a large number of microarray experiments can provide insights that cannot be gained by analyzing single experiments. However, such a meta-analysis poses significant challenges with respect to data comparability and normalization, systematic sample annotation, and analysis tools. Genevestigator addresses these issues using a large curated expression database and a set of specifically developed analysis tools that are accessible over the internet. This combination has already proven to be useful in the area of plant research based on a large set of Arabidopsis data (Grennan, 2006). Here, we present the release of the Genevestigator rice and barley gene expression databases that contain quality-controlled and well annotated microarray experiments using ontologies. The databases currently comprise experiments from pathology, plant nutrition, abiotic stress, hormone treatment, genotype, and spatial or temporal analysis, but are expected to cover a broad variety of research areas as more experimental data become available. The transcriptome meta-analysis of the model species rice and barley is expected to deliver results that can be used for functional genomics and biotechnological applications in cereals.