Markus Weniger

Publications of Markus Weniger

• Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis.

  Authors: Steffen Blenk, Julia C Engelmann, Stefan Pinkert, Markus Weniger, Jörg Schultz, Andreas Rosenwald, Hans K Müller-Hermelink, Tobias Müller, Thomas Dandekar

  BMC cancer. 02/2008; 8:106.

  BACKGROUND: Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocationBACKGROUND: Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. METHODS: We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. RESULTS: On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. CONCLUSION: The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.

• Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

  Authors: Steffen Blenk, Julia Engelmann, Stefan Pinkert, Markus Weniger, Jörg Schultz, Andreas Rosenwald, Hans Müller-Hermelink, Tobias Müller, Thomas Dandekar

  BMC Cancer. 01/2008;

  Abstract Background Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmarkAbstract Background Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. Methods We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. Results On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. Conclusion The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.

• Genome Expression Pathway Analysis Tool--analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context.

  Authors: Markus Weniger, Julia C Engelmann, Jörg Schultz

  BMC bioinformatics. 02/2007; 8:179.

  BACKGROUND: Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure theBACKGROUND: Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. RESULTS: We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://gepat.sourceforge.net. CONCLUSION: GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at http://gepat.bioapps.biozentrum.uni-wuerzburg.de.

• Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

  Authors: Markus Weniger, Julia Engelmann, Jörg Schultz

  BMC Bioinformatics. 01/2007;

  Abstract Background Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used toAbstract Background Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. Results We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://gepat.sourceforge.net . Conclusion GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at http://gepat.bioapps.biozentrum.uni-wuerzburg.de .

• Genome Expression Pathway Analysis Tool - Analyse und Visualisierung von Microarray Genexpressionsdaten unter genomischen, proteomischen und metabolischen Gesichtspunkten

  Authors: Markus Weniger

  Die Messung der Genexpression ist für viele Bereiche der Biologie und Medizin wichtig geworden und unterstützt Studien über Behandlung, Krankheiten und Entwicklungsstadien. Microarrays könnenDie Messung der Genexpression ist für viele Bereiche der Biologie und Medizin wichtig geworden und unterstützt Studien über Behandlung, Krankheiten und Entwicklungsstadien. Microarrays können verwendet werden, um die Expression von tausenden mRNA-Molekülen gleichzeitig zu messen und ermöglichen so einen Einblick und einen Vergleich der verschiedenen zellulären Bedingungen. Die Daten, die durch Microarray-Experimente gewonnen werden, sind hochdimensional und verrauscht, eine Interpretation der Daten ist deswegen nicht einfach. Obwohl Programme für die statistische Auswertung von Microarraydaten existieren, fehlt vielen eine Integration der Analyseergebnisse mit einer automatischen Interpretationsmöglichkeit. In dieser Arbeit wurde GEPAT, Genome Expression Pathway Analysis Tool, entwickelt, das eine Analyse der Genexpression unter dem Gesichtspunkten der Genomik, Proteomik und Metabolik ermöglicht. GEPAT integriert statistische Methoden zum Datenimport und -analyse mit biologischer Interpretation für Genmengen oder einzelne Gene, die auf dem Microarray gemessen werden. Verschiedene Typen von Oligonukleotid- und cDNAMicroarrays können importiert werden, unterschiedliche Normalisierungsmethoden können auf diese Daten angewandt werden, anschließend wird eine Datenannotation durchgeführt. Nach dem Import können mit GEPAT verschiedene statische Datenanalysemethoden wie hierarchisches, k-means und PCA-Clustern, ein auf einem linearen Modell basierender t-Test, oder ein Vergleich chromosomaler Profile durchgeführt werden. Die Ergebnisse der Analysen können auf Häufungen biologischer Begriffe und Vorkommen in Stoffwechselwegen oder Interaktionsnetzwerken untersucht werden. Verschiedene biologische Datenbanken wurden integriert, um zu jeder Gensonde auf dem Array Informationen zur Verfügung stellen zu können. GEPAT bietet keinen linearen Arbeitsablauf, sondern erlaubt die Benutzung von beliebigen Teilmengen von Genen oder biologischen Proben als Startpunkt einer neuen Analyse oder Interpretation. Dabei verlässt es sich auf bewährte Datenanalyse-Pakete, bietet einen modularen Ansatz zur einfachen Erweiterung und kann auf einem verteilten Computernetzwerk installiert werden, um eine große Zahl an Benutzern zu unterstützen. Es ist unter der LGPL Open-Source Lizenz frei verfügbar und kann unter http://gepat.sourceforge.net heruntergeladen werden. The measurement of gene expression data is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and noisy, and interpretation of the results can get tricky. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. In this work GEPAT, Genome Expression Pathway Analysis Tool, was developed, offering an analysis of gene expression data under genomic, proteomic and metabolic context. GEPAT integrates statistical methods for data import and data analysis together with an biological interpretation for subset of genes or single genes measured on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-Test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various informations for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as start for a new data analysis or interpretation. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://gepat.sourceforge.net.