Showing papers by "Hans-Peter Lenhof published in 2007"

GeneTrail—advanced gene set enrichment analysis

Abstract: We present a comprehensive and efficient gene set analysis tool, called 'GeneTrail' that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, 'Over-Representation Analysis' (ORA) comparing a reference set of genes to a test set, and 'Gene Set Enrichment Analysis' (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.de.

Frequent overexpression of the genes FXR1, CLAPM1 and EIF4G located on amplicon 3q26-27 in squamous cell carcinoma of the lung.

Abstract: Previously, we reported gene amplification at chromosome 3q26-27 in more than one third of squamous cell carcinomas of the lung. Frequent amplification of eukaryotic translation initiation factor 4G on 3q27.1 indicated a possible role of this amplification in translation initiation. The analysis of 61 squamous cell lung carcinomas shows that the percentage of carcinomas with a 3q27.1 amplification increases in higher malignant tumors. Non-invasive (T1) and minimal-invasive (T2) tumor stages showed similar percentages of amplified and non-amplified tumors, whereas locally-invasive (T3) tumors revealed a statistically significant (p < 0.05) increased percentage of amplified tumors. Microarrays were used to analyze the expression pattern of genes mapping in the amplified domain and its flanking regions (3q25-28) as well as the expression of genes directly or indirectly associated with translation initiation in squamous cell carcinoma, large cell carcinoma, adenocarcinoma and small cell carcinoma. Three genes, namely FXR1, CLAPM1 and EIF4G, are most frequently overexpressed in the center of the amplified domain in squamous cell carcinomas. The eukaryotic translation initiation factors 4A1, 2B and 4B as well as the poly(A)-binding protein PABPC1 where found to be overexpressed in all lung cancer entities. We found, however, no overexpression of eIF4E. Our results contribute to the understanding of the frequent amplification processes in squamous cell carcinomas of the lung and to the understanding of the translation initiation that appears not to require eIF4E in lung carcinogenesis.

Computation of significance scores of unweighted Gene Set Enrichment Analyses

Abstract: Background: Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any sorted list of genes. Given the gene list and an arbitrary biological category, GSEA evaluates whether the genes of the considered category are randomly distributed or accumulated on top or bottom of the list. Usually, significance scores (p-values) of GSEA are computed by nonparametric permutation tests, a time consuming procedure that yields only estimates of the p-values. Results: We present a novel dynamic programming algorithm for calculating exact significance values of unweighted Gene Set Enrichment Analyses. Our algorithm avoids typical problems of nonparametric permutation tests, as varying findings in different runs caused by the random sampling procedure. Another advantage of the presented dynamic programming algorithm is its runtime and memory efficiency. To test our algorithm, we applied it not only to simulated data sets, but additionally evaluated expression profiles of squamous cell lung cancer tissue and autologous unaffected tissue.

Electrostatic potentials of proteins in water: a structured continuum approach

Abstract: Electrostatic interactions play a crucial role in many biomolecular processes, including molecular recognition and binding. Biomolecular electrostatics is modulated to a large extent by the water surrounding the molecules. Here, we present a novel approach to the computation of electrostatic potentials which allows the inclusion of water structure into the classical theory of continuum electrostatics. Based on our recent purely differential formulation of nonlocal electrostatics [Hildebrandt, et al. (2004) Phys. Rev. Lett., 93, 108104] we have developed a new algorithm for its efficient numerical solution. The key component of this algorithm is a boundary element solver, having the same computational complexity as established boundary element methods for local continuum electrostatics. This allows, for the first time, the computation of electrostatic potentials and interactions of large biomolecular systems immersed in water including effects of the solvent's structure in a continuum description. We illustrate the applicability of our approach with two examples, the enzymes trypsin and acetylcholinesterase. The approach is applicable to all problems requiring precise prediction of electrostatic interactions in water, such as protein--ligand and protein--protein docking, folding and chromatin regulation. Initial results indicate that this approach may shed new light on biomolecular electrostatics and on aspects of molecular recognition that classical local electrostatics cannot reveal. Contact: anhi@bioinf.uni-sb.de

BNDB - the Biochemical Network Database.

Abstract: Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources. We present the Biochemical Network Database (BNDB), a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB. BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org .

Evidence for the mode of action of the highly cytotoxic Streptomyces polyketide kendomycin.

Abstract: The macrocyclic polyketide kendomycin exhibits antiosteoporotic and antibacterial activity, as well as strong cytotoxicity against multiple human tumor cell lines. Despite the promise of this compound in several therapeutic areas, the cellular target(s) of kendomycin have not been identified to date. We have used a number of approaches, including microscopy, proteomics, and bioinformatics, to investigate the mode of action of kendomycin in mammalian cell cultures. In response to kendomycin treatment, human U-937 tumor cells exhibit depolarization of the mitochondrial membrane, caspase 3 activation, and DNA laddering, consistent with induction of the intrinsic apoptotic pathway. To elucidate possible apoptotic triggers, DIGE and MALDI-TOF were used to identify proteins that are differently regulated in U-937 cells relative to controls. Statistical analysis of the proteomics data by the new web-based application GeneTrail highlighted several significant changes in protein expression, most notably among proteasomal regulatory subunits. Overall, the profile of altered expression closely matches that observed with other tumor cell lines in response to proteasome inhibition. Direct assay in vitro further shows that kendomycin inhibits the chymotrypsin-like activity of the rabbit reticulocyte proteasome, with comparable efficacy to the established inhibitor MG-132. We have also demonstrated that ubiquitinylated proteins accumulate in kendomycin-treated U-937 cells, while vacuolization of the endoplasmic reticulum and mitochondrial swelling are induced in a second cell line derived from kangaroo rat epithelial (PtK(2)) cells, phenotypes classically associated with inhibition of the proteasome. This study therefore provides evidence that kendomycin mediates its cytotoxic effects, at least in part, through proteasome inhibition.

SePaCS—a web-based application for classification of seroreactivity profiles

Abstract: Immunogenic antigen sets possess high potential for minimally invasive disease detection and monitoring. For various diseases, including cancer, appropriate antigen sets have already been detected in blood sera of patients. Typically, a large number of sera from diseased and unaffected persons is screened for the antigens of interest. Sophisticated statistical learning approaches are trained on the resulting data set to classify sera as either tumor or normal sera. We developed a web-based application, called ‘Seroreactivity Profile Classification Service’ (SePaCS) that enables clinical groups to carry out analyzes of training sets and predictions of unclassified seroreactivity profiles with minimal effort. SePaCS provides a broad range of classification methods: four versions of a Naive Bayes Classifier, Support Vector Machines with a radial basis function kernel, Linear Discriminant Analysis, and Diagonal Discriminant Analysis. The computed results are summarized in a PDF file. We demonstrate the functionality of SePaCS exemplarily for meningioma, a generally benign intracranial tumor. As a second example, we evaluated SePaCS on glioma, a malignant brain tumor. SePaCS is freely available at http://www.bioinf.uni-sb.de/sepacs.

Abstract analysis of pathways using the BN++ software framework

Abstract: analysis of pathways using the BN++ software framework Jan Küntzer*1, Benny Kneissl1, Oliver Kohlbacher2 and Hans-Peter Lenhof1 Address: 1Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany and 2Center for Bioinformatics/Wilhelm Schickard Institute for Computer Science, Eberhard-Karls-Universität Tübingen, 72076 Tübingen, Germany Email: Jan Küntzer* kuentzer@bioinf.uni-sb.de * Corresponding author Introduction Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a vast amount of life science data, including pathway information such as metabolic and regulatory pathways. The rapid increase of these data for various organisms offers the possibility to perform analyses on the networks for single organisms (intra-species) as well as across different organisms (inter-species). However, the sheer amount and heterogeneity of the data pose a major challenge and call for an integrative system, allowing to manage all this information. With BN++, especially its C++ framework, we presented such a system [2]. In contrast to databases (e.g. KEGG, Reactome, IntAct,...), which offer only predefined analyses such as minimal connected component or pathway detection from a start to an end compound, the mathematical graph representation in BN++ allows additionally the implementation of own routines. The analysis of biochemical pathway information has different applications, e.g. in the process of target identification, drug design and in the search for causes of genetic diseases. Therefore, nodes or edges are removed and alternative pathways in an organism need to be identified. In basic research these networks can be used for the comparison of metabolic processes of different organisms. For example the information on the metabolism of one organism can be used to understand the newly sequenced genome (and, hence the metabolic pathways) of another organism as presented in [1]. Background To understand the mathematical graph representation, we first need to define some concepts: We define G(V, E) to be a mathematical graph, where V denotes a finite set of nodes of G and E = (V × V) denotes a set of pairs of nodes, called the edges of the G. A graph G(V, E) is defined as bipartite, iff V = V1 ∪ V2 can be partitioned into two sets V1 and V2 such that V1 V2 = ∅ and (u, v) ∈ E = ((V1 × V2) ∪ (V2 × V1)) implies either u ∈ V1 and v ∈ V2, or u ∈ V2 and v ∈ V1. The modeling of a biochemical pathway as a mathematical graph can be done in different ways, differing in the interpretation of the nodes and edges. Hence, various data models have been developed over the last years. The mostly used models are presented in [3]. We will define three different models in the following: First, we define a bipartite reaction graph to be a bipartite graph, where V1 contains all events and V2 all compounds. A node A is connected with a directed edge to node B, iff compound A plays the role of an educt in event B or if compound B plays the role of a product in event A. Second, a compound graph is defined as a mathematical graph, where the nodes are the chemical compounds. A is connected with a directed edge to B, iff compound A plays the role of an educt and compound B plays the role of a product in the same event. Third, a event graph defines a mathematical graph, where the nodes are the events. A node A is connected with a directed edge to B, iff a compound Y plays the role of an educt in the event A and the role of a product in the event B. from BioSysBio 2007: Systems Biology, Bioinformatics and Synthetic Biology Manchester, UK. 11–13 January 2007 Published: 8 May 2007 BMC Systems Biology 2007, 1(Suppl 1):P24 doi:10.1186/1752-0509-1-S1-P24 John Cumbers, Xu Gu, Jong Sze Wong Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1752-0509-1-S1-info.pdf This abstract is available from: http://www.biomedcentral.com/1752-0509/1?issue=S1 © 2007 Küntzer et al; licensee BioMed Central Ltd.