AltAnalyze and DomainGraph: analyzing and visualizing exon expression data

Alternative splicing is an important mechanism for increasing protein diversity. However, its functional effects are largely unknown. Here, we present our new software workflow composed of the open-source application AltAnalyze and the Cytoscape plugin DomainGraph. Both programs provide an intuitive and comprehensive end-to-end solution for the analysis and visualization of alternative splicing data from Affymetrix Exon and Gene Arrays at the level of proteins, domains, microRNA binding sites, molecular interactions and pathways. Our software tools include easy-to-use graphical user interfaces, rigorous statistical methods (FIRMA, MiDAS and DABG filtering) and do not require prior knowledge of exon array analysis or programming. They provide new methods for automatic interpretation and visualization of the effects of alternative exon inclusion on protein domain composition and microRNA binding sites. These data can be visualized together with affected pathways and gene or protein interaction networks, allowing a straightforward identification of potential biological effects due to alternative splicing at different levels of granularity. Our programs are available at http://www.altanalyze.org and http://www.domaingraph.de. These websites also include extensive documentation, tutorials and sample data

Structure Collisions between Interacting Proteins

Protein-protein interactions take place at defined binding interfaces. One protein may bind two or more proteins at different interfaces at the same time. So far it has been commonly accepted that non-overlapping interfaces allow a given protein to bind other proteins simultaneously while no collisions occur between the binding protein structures. To test this assumption, we performed a comprehensive analysis of structural protein interactions to detect potential collisions. Our results did not indicate cases of biologically relevant collisions in the Protein Data Bank of protein structures. However, we discovered a number of collisions that originate from alternative protein conformations or quaternary structures due to different experimental conditions

DASMI: exchanging, annotating and assessing molecular interaction data

Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet

Neuartige Analyseansätze für kontext-abhängige molekulare Netzwerke

Proteins are key players in all kinds of biological processes and accurate knowledge of their presence and their interactions is fundamental for understanding the functioning of the cells. Over the last years, many large-scale studies have been performed in order to unravel the complete human interactome. However, the results of these studies usually depend on the cellular conditions, in which the protein interactions were detected. Furthermore, additional biological mechanisms or temporal and spatial constraints contribute to the context-dependent formation of protein interactions. In this thesis, we focus on different biological aspects that are important for the formation of protein-protein interactions. We first analyze protein interactions in a structural context and demonstrate that interacting proteins may collide in three-dimensional space, rendering the interaction impossible. Second, we investigate the tissue-specific formation of protein interactions. We analyze the ability of different technologies such as microarray platforms and next-generation RNA-sequencing to reliably detect tissue-specific gene expression. We further use gene expression data to identify tissue-specific protein interactions and their functional implications. Finally, we concentrate on protein variants that arise by alternative splicing events. We describe our software DomainGraph that allows for visually exploring protein variants and their interactions in different biological conditions.Proteine übernehmen viele wichtige Funktionen in biologischen Prozessen. Daher ist genaues Wissen über ihre Interaktionen essentiell, um die Funktionsweise von Zellen zu verstehen. In den letzten Jahren wurden viele Experimente durchgeführt, um die Gesamtheit des menschlichen Interaktoms zu ermitteln. Die Ergebnisse solcher Studien sind jedoch abhängig von der biologischen Umgebung, in der die Proteininteraktionen nachgewiesen wurden. Außerdem werden viele Proteininteraktionen aufgrund zeitlicher und räumlicher Einschränkungen nur in einem bestimmten biologischen Kontext gebildet. In dieser Arbeit betrachten wir verschiedene biologische Aspekte, die eine wichtige Rolle für die Interaktionen zwischen Proteinen spielen können. Zuerst analysieren wir Proteininteraktionen in einem strukturellen Kontext. Wir zeigen auf, dass interagierende Proteine im dreidimensionalen Raum kollidieren können und dadurch Interaktionen verhindert werden können. Des Weiteren untersuchen wir die gewebespezifische Ausbildung von Proteininteraktionen. In diesem Zusammenhang vergleichen wir zunächst Möglichkeiten, Genexpression mit Hilfe verschiedener Technologien wie Microarrayanalyse und Hochdurchsatz-Sequenzierung zu detektieren. Die Ergebnisse dieser Studie benutzen wir, um gewebespezifische Proteininteraktionen zu identifizieren und diese funktionell zu charakterisieren. Im letzten Teil der Arbeit konzentrieren wir uns auf Proteinvarianten, die sich durch alternatives Spleißen ergeben. Wir beschreiben unsere Software DomainGraph, die die visuelle Analyse von Proteinvarianten und deren Interaktionen unter verschiedenen biologischen Bedingungen ermöglicht

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results