Rechnergestützte Analyse und Interpretation prokaryotischer Hochdurchsatz-Expressiondaten

After preprocessing microarray raw expression data normally their functional interpretation follows. Performing this manually is time-consuming and it is hard to get an overview on the relevant functions this way. Therefore, in this thesis an integrative program suite (JProGO) for the functional interpretation of prokaryotic gene expression data based on the Gene Ontology (GO) was developed. It identifies biological functions and processes that significantly differ in their expression profiles when comparing two experimental conditions. JProGO supports more than 20 prokaryotic species and offers several different statistical tests: the cut-off based Fisher's exact test and the cut-off free Student's t-test, Kolmogorov-Smirnov (KS-test) and unpaired Wilcoxon test (U-test). Further features are methods of correcting the multiple testing effect and the support of gene synonyms and of different types of expression data. Obtained results can be visualized as a subgraph of GO. The tool was tested with expression data from Escherichia coli and Bacillus subtilis. First, the influence of the threshold value for the Fisher's exact test was elucidated. Then, the cut-off free methods were comparatively evaluated, whereas the U-test was found as a good alternative to the KS- and t-test. After this evaluation, JProGO was used for the analysis of in-house expression data from the pathogen Pseudomonas aeruginosa. The impact of different preprocessing algorithms on the results of the JProGO-based analysis was investigated. Furthermore, several significant GO nodes were identified that fit the expectation on the experiments. Finally, the approach was expanded from GO terms towards regulons. Expression data from E. coli transcription factor knockout strains were evaluated and the results were in good agreement with the expectation. Here, the KS-test performed best, whereas the U-test was almost as good.Nach der Präprozessierung von Mikroarray-Expressionsdaten erfolgt normalerweise ihre funktionelle Interpretation. Dieses manuell durchzuführen, ist zeitraubend und ein Überblick über die relevanten Funktionen kann so nur schwer gewonnen werden. Daher wurde in dieser Arbeit eine integrative Software-Suite (JProGO) zur funktionellen Auswertung von prokaryotischen Expressiondaten basierend auf der Gene Ontology (GO) entwickelt. Sie identifiziert biologische Funktionen und Prozesse, die sich beim Vergleich zweier Versuchsbedingungen in ihrem Expressionsprofil signifikant unterscheiden. JProGO unterstützt mehr als 20 prokaryotische Spezies und bietet verschiedene statistische Tests an: den Schwellenwert-basierten exakten Fisher-Test und den Schwellenwert-freien t-, Kolmogorov-Smirnov- (KS-Test) und Mann-Whitney U-Test (U-Test). Weitere Funktionen sind Methoden zur Korrektur des multiplen Testeffekts sowie die Unterstützung von Gen-Synonymen und verschiedenen Expressionsdaten-Typen. Die Ergebnisse können als Untergraph von GO visualisiert werden. Das Programm wurde mit Expressionsdaten von Escherichia coli und Bacillus subtilis getestet. Zuerst wurde der Einfluss des Schwellenwertes beim exakten Fisher-Test untersucht. Dann wurden die Schwellenwert-freien Methoden evaluiert, wobei sich der U-Test als gute Alternative zum KS- und t-Test erwies. Nach dieser Evaluierung wurde JProGO für die Analyse von in-house Expressionsdaten des Pathogens Pseudomonas aeruginosa eingesetzt. Es wurde der Einfluss verschiedener Präprozessierungsalgorithmen auf die JProGO-basierte Auswertung untersucht. Zudem wurden einige signifikante GO-Knoten gefunden, die der Erwartung an die Experimente entsprechen. Zuletzt wurde der Ansatz von GO-Gruppen auf Regulons erweitert. Expressionsdaten von E. coli-Stämmen mit ausgeschalteten Transkriptionsfaktoren wurden evaluiert; die Ergebnisse entsprachen der Erwartung gut. Hierbei schnitt der KS-Test am besten ab, dicht gefolgt vom U-Test

JCat: a novel tool to adapt codon usage of a target gene to its potential expression host

A novel method for the adaptation of target gene codon usage to most sequenced prokaryotes and selected eukaryotic gene expression hosts was developed to improve heterologous protein production. In contrast to existing tools, JCat (Java Codon Adaptation Tool) does not require the manual definition of highly expressed genes and is, therefore, a very rapid and easy method. Further options of JCat for codon adaptation include the avoidance of unwanted cleavage sites for restriction enzymes and Rho-independent transcription terminators. The output of JCat is both graphically and as Codon Adaptation Index (CAI) values given for the pasted sequence and the newly adapted sequence. Additionally, a list of genes in FASTA-format can be uploaded to calculate CAI values. In one example, all genes of the genome of Caenorhabditis elegans were adapted to Escherichia coli codon usage and further optimized to avoid commonly used restriction sites. In a second example, the Pseudomonas aeruginosa exbD gene codon usage was adapted to E.coli codon usage with parallel avoidance of the same restriction sites. For both, the degree of introduced changes was documented and evaluated. JCat is integrated into the PRODORIC database that hosts all required information on the various organisms to fulfill the requested calculations. JCat is freely 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 Fisher's exact test, as well as the threshold value-independent Kolmogorov–Smirnov and Student's 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 Wilcoxon's 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

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

PrediSi: prediction of signal peptides and their cleavage positions

We have developed PrediSi (Prediction of Signal peptides), a new tool for predicting signal peptide sequences and their cleavage positions in bacterial and eukaryotic amino acid sequences. In contrast to previous prediction tools, our new software is especially useful for the analysis of large datasets in real time with high accuracy. PrediSi allows the evaluation of whole proteome datasets, which are currently accumulating as a result of numerous genome projects and proteomics experiments. The method employed is based on a position weight matrix approach improved by a frequency correction which takes in to consideration the amino acid bias present in proteins. The software was trained using sequences extracted from the most recent version of the SwissProt database. PrediSi is accessible via a web interface. An extra Java package was designed for the integration of PrediSi into other software projects. The tool is freely available on the World Wide Web at http://www.predisi.de

Biocompatibility of photopolymers for additive manufacturing

To establish photopolymers for the production of class II or class III medical products by additive manufacturing it is essential to know which components of photopolymeric systems, consisting of monomers, photoinitiators and additives, are the determining factors on their biocompatible properties. In this study the leachable substances of a cured photopolymeric system were eluted and identified by HPLC-MS detection. In addition the cured photopolymer was testes for cytotoxicity and genotoxicity according to DIN EN ISO 10993 for long time applications. The results showed that uncured residual monomers are the determining factor on the biocompatible properties of the photopolymeric system. Strategies to reduce these residual monomers in the cured photopolymer are presented

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Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes

A new online framework for the accurate and integrative prediction of transcription factor binding sites (TFBSs) in prokaryotes was developed. The system consists of three interconnected modules: 1. The PRODORIC database as a comprehensive data source and extensive collection of TFBSs with corresponding position weight matrices (PWMs). 2. The pattern matching tool Virtual Footprint for the prediction of genome based regulons and for the analysis of individual promoter regions. 3. The interactive genome browser GBPro for the visualization of TFBS search results in their genomic context and links to gene and regulator-specific information in PRODORIC. The aim of this service is to provide researchers a free and easy to use collection of interconnected tools in the field of molecular microbiology, infection and systems biology