Claudia Choi

TRANSPATH ® : an integrated database on signal transduction and a tool for array analysis

TRANSPATH is a database system about gene regulatory networks that combines encyclopedic information on signal transduction with tools for visualization and analysis. The integration with TRANSFAC, a database about transcription factors and their DNA binding sites, provides the possibility to obtain complete signaling pathways from ligand to target genes and their products, which may themselves be involved in regulatory action. As of July 2002, the TRANSPATH Professional release 3.2 contains about 9800 molecules, >1800 genes and >11 400 reactions collected from approximately 5000 references. With the ArrayAnalyzer, an integrated tool has been developed for evaluation of microarray data. It uses the TRANSPATH data set to identify key regulators in pathways connected with up- or down-regulated genes of the respective array. The key molecules and their surrounding networks can be viewed with the PathwayBuilder, a tool that offers four different modes of visualization. More information on TRANSPATH is available at http://www.biobase.de/pages/products/databases.html.

SYSTOMONAS — an integrated database for systems biology analysis of Pseudomonas

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at .

JProGO: a novel tool for the functional interpretation of prokaryotic microarray data using Gene Ontology information

A novel program suite was implemented for the functional interpretation of high-throughput gene expression data based on the identification of Gene Ontology (GO) nodes. The focus of the analysis lies on the interpretation of microarray data from prokaryotes. The three well established statistical methods of the threshold value-based Fishers exact test, as well as the threshold value-independent Kolmogorov–Smirnov and Students t-test were employed in order to identify the groups of genes with a significantly altered expression profile. Furthermore, we provide the application of the rank-based unpaired Wilcoxons test for a GO-based microarray data interpretation. Further features of the program include recognition of the alternative gene names and the correction for multiple testing. Obtained results are visualized interactively both as a table and as a GO subgraph including all significant nodes. Currently, JProGO enables the analysis of microarray data from more than 20 different prokaryotic species, including all important model organisms, and thus constitutes a useful web service for the microbial research community. JProGO is freely accessible via the web at the following address:

TRANSPATH®—A High Quality Database Focused on Signal Transduction

TRANSPATH® can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyser. Therefore, three modules have been created: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder™, which provides several different types of network visualization and hence faciliates understanding; the third is ArrayAnalyzer™, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH® to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH® is the inclusion of transcription factor–gene relations, which are transferred from TRANSFAC®, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes. More information is available at www.biobase.de/pages/products/databases.html.

Combination of a data warehouse concept with web services for the establishment of the Pseudomonas systems biology database SYSTOMONAS

Summary Systems biology requires the integration of data from various sources and their combined interpretation using different bioinformatics tools. Integration of different biological databases, however, is often problematic due to their semantic and structural diversity. Moreover, necessary continuous updates of both the structure and content of a database provide further challenges for an integration process. We established the novel database SYSTOMONAS for SYSTems biology of pseudOMONAS by integrating heterogeneous data from highly different external resources including BioCyc, BRENDA, ENZYME, Pseudomonas Genome Database v2, KEGG, and PRODORIC. For this purpose we combined a data warehouse concept with the advantages of web services. This hybrid approach benefits from the fast performance and data consistency provided by the data warehouse system and from the up-to-dateness ensured by use of dynamic web services. The data warehouse part is realized by ETL processes (Extract, Transform, Load), during which data are checked for consistency and standardized to ensure their integrity. While accessing SYSTOMONAS via the internet, parts of the data warehouse content are dynamically enriched using the web service part of the system via SOAP (originally for Simple Object Access Protocol) interfaces with BRENDA, KEGG and PRODORIC. SYSTOMONAS is designed to integrate in-house experimental high-throughput data with up-to-date information available in the mentioned public databases. SYSTOMONAS also serves as a repository for the prediction of metabolic and regulatory networks. SYSTOMONAS is accessible at http://www.systomonas.de.

A Likelihood Ratio Test for Differential Metabolic Profiles in Multiple Intensity Measurements

High throughput technologies like transcriptomics using DNA arrays or metabolomics employing a combination of gas chromatography with mass spectrometry provide valuable information about cellular processes. However, the measurements are often highly corrupted with noise of the experimental data which makes it sometimes difficult to draw reliable conclusions. Therefore, suitable statistical methods are needed for the evaluation of the experimental data to distinguish changes caused by biological phenomena from random variations due to noise. This paper introduces a likelihood ratio test to multiple metabolome measurements. The method was tested to differentiate differential metabolite compositions obtained from the pathogenic bacterium Pseudomonas aeruginosagrown under various environmental conditions.

Analysis of metabolome data by a maximum likelihood approach

Metabolomics emerges as one key aspect of systems biology, since quantifying the dynamic set of metabolites reveals the effect of altered gene expression and protein pattern and thus complements transcriptomics and proteomics. By high-throughput techniques, such as measuring metabolites by gas chromatography-mass spectrometry (GC-MS), enormous data amounts are produced, that need to be analysed. At present, a variety of methods are available for cluster analysis of metabolome data.