FunSimMat update: new features for exploring functional similarity

Quantifying the functional similarity of genes and their products based on Gene Ontology annotation is an important tool for diverse applications like the analysis of gene expression data, the prediction and validation of protein functions and interactions, and the prioritization of disease genes. The Functional Similarity Matrix (FunSimMat, http://www.funsimmat.de) is a comprehensive database providing various precomputed functional similarity values for proteins in UniProtKB and for protein families in Pfam and SMART. With this update, we significantly increase the coverage of FunSimMat by adding data from the Gene Ontology Annotation project as well as new functional similarity measures. The applicability of the database is greatly extended by the implementation of a new Gene Ontology-based method for disease gene prioritization. Two new visualization tools allow an interactive analysis of the functional relationships between proteins or protein families. This is enhanced further by the introduction of an automatically derived hierarchy of annotation classes. Additional changes include a revised user front-end and a new RESTlike interface for improving the user-friendliness and online accessibility of FunSimMat

Ontology-based similarity measures and their application in bioinformatics

Genome-wide sequencing projects of many different organisms produce large numbers of sequences that are functionally characterized using experimental and bioinformatics methods. Following the development of the first bio-ontologies, knowledge of the functions of genes and proteins is increasingly made available in a standardized format. This allows for devising approaches that directly exploit functional information using semantic and functional similarity measures. This thesis addresses different aspects of the development and application of such similarity measures. First, we analyze semantic and functional similarity measures and apply them for investigating the functional space in different taxa. Second, a new software program and a new database are described, which overcome limitations of existing tools and simplify the utilization of similarity measures for different applications. Third, we delineate two applications of our functional similarity measures. We utilize them for analyzing domain and protein interaction datasets and derive thresholds for grouping predicted domain interactions into low- and high-confidence subsets. We also present the new MedSim method for prioritization of candidate disease genes, which is based on the observation that genes and proteins contributing to similar diseases are functionally related. We demonstrate that the MedSim method performs at least as well as more complex state-of-the-art methods and significantly outperforms current methods that also utilize functional annotation.Die Sequenzierung der kompletten Genome vieler verschiedener Organismen liefert eine große Anzahl an Sequenzen, die mit Hilfe experimenteller und bioinformatischer Methoden funktionell charakterisiert werden. Nach der Entwicklung der ersten Bio-Ontologien wird das Wissen über die Funktionen von Genen und Proteinen zunehmend in einem standardisierten Format zur Verfügung gestellt. Dadurch wird die Entwicklung von Verfahren ermöglicht, die funktionelle Informationen direkt mit Hilfe semantischer und funktioneller Ähnlichkeit verwenden. Diese Doktorarbeit befasst sich mit verschiedenen Aspekten der Entwicklung und Anwendung solcher Ähnlichkeitsmaße. Zuerst analysieren wir semantische und funktionelle Ähnlichkeitsmaße und verwenden sie für eine Analyse des funktionellen Raumes verschiedener Organismengruppen. Danach beschreiben wir eine neue Software und eine neue Datenbank, die Limitationen existierender Programme überwinden und den Einsatz von Ähnlichkeitsmaßen in verschiedenen Anwendungen vereinfachen. Drittens schildern wir zwei Anwendungen unserer funktionellen Ähnlichkeitsmaße. Wir verwenden sie zur Analyse von Domän- und Proteininteraktionsdatensätzen und leiten Grenzwerte ab, um die Domäninteraktionen in Teilmengen mit niedriger und hoher Konfidenz einzuteilen. Außerdem präsentieren wir die MedSim-Methode zur Priorisierung von potentiellen Krankheitsgenen. Sie beruht auf der Beobachtung, dass Gene und Proteine, die zu ähnlichen Krankheiten beitragen, funktionell verwandt sind. Wir zeigen, dass die MedSim-Methode mindestens so gut funktioniert wie komplexere moderne Methoden und die Leistung anderer aktueller Methoden signifikant übertrifft, die auch funktionelle Annotationen verwenden

GOTax: investigating biological processes and biochemical activities along the taxonomic tree

GOTax, a novel web-based platform that integrates protein annotation with protein family classification and taxonomy, allows for an extensive assessment of functional similarity between proteins and for comparing and analyzing the distribution of protein families and protein functions over different taxonomic groups

Applications of semantic similarity measures

There has been much interest in uncovering protein-protein interactions and their underlying domain-domain interactions. Many experimental techniques have been developed, for example yeast-two-hybrid screening and tandem affinity purification. Since it is time consuming and expensive to perform exhaustive experimental screens, in silico methods are used for predicting interactions. However, all experimental and computational methods have considerable false positive and false negative rates. Therefore, it is necessary to validate experimentally determined and predicted interactions. One possibility for the validation of interactions is the comparison of the functions of the proteins or domains. Gene Ontology (GO) is widely accepted as a standard vocabulary for functional terms, and is used for annotating proteins and protein families with biological processes and their molecular functions. This annotation can be used for a functional comparison of interacting proteins or domains using semantic similarity measures. Another application of semantic similarity measures is the prioritization of disease genes. It is know that functionally similar proteins are often involved in the same or similar diseases. Therefore, functional similarity is used for predicting disease associations of proteins. In the first part of my talk, I will introduce some semantic and functional similarity measures that can be used for comparison of GO terms and proteins or protein families. Then, I will show their application for determining a confidence threshold for domain-domain interaction predictions. Additionally, I will present FunSimMat (http://www.funsimmat.de/), a comprehensive resource of functional similarity values available on the web. In the last part, I will introduce the problem of comparing diseases, and a first attempt to apply functional similarity measures based on GO to this problem

Molecular basis of telaprevir resistance due to V36 and T54 mutations in the NS3-4A protease of the hepatitis C virus

Structural analysis of the inhibitor Telaprevir (VX-950) of the hepatitis C virus (HCV) protease NS3-4A shows that mutations at V36 and/or T54 result in impaired interaction with VX-950, explaining the development of viral breakthrough variants

Functional inhibition of acid sphingomyelinase by Fluphenazine triggers hypoxia-specific tumor cell death

Owing to lagging or insufficient neo-angiogenesis, hypoxia is a feature of most solid tumors. Hypoxic tumor regions contribute to resistance against antiproliferative chemotherapeutics, radiotherapy and immunotherapy. Targeting cells in hypoxic tumor areas is therefore an important strategy for cancer treatment. Most approaches for targeting hypoxic cells focus on the inhibition of hypoxia adaption pathways but only a limited number of compounds with the potential to specifically target hypoxic tumor regions have been identified. By using tumor spheroids in hypoxic conditions as screening system, we identified a set of compounds, including the phenothiazine antipsychotic Fluphenazine, as hits with novel mode of action. Fluphenazine functionally inhibits acid sphingomyelinase and causes cellular sphingomyelin accumulation, which induces cancer cell death specifically in hypoxic tumor spheroids. Moreover, we found that functional inhibition of acid sphingomyelinase leads to overactivation of hypoxia stress-response pathways and that hypoxia-specific cell death is mediated by the stress-responsive transcription factor ATF4. Taken together, the here presented data suggest a novel, yet unexplored mechanism in which induction of sphingolipid stress leads to the overactivation of hypoxia stress-response pathways and thereby promotes their pro-apoptotic tumor-suppressor functions to specifically kill cells in hypoxic tumor areas

Improving disease gene prioritization using the semantic similarity of Gene Ontology terms

Motivation: Many hereditary human diseases are polygenic, resulting from sequence alterations in multiple genes. Genomic linkage and association studies are commonly performed for identifying disease-related genes. Such studies often yield lists of up to several hundred candidate genes, which have to be prioritized and validated further. Recent studies discovered that genes involved in phenotypically similar diseases are often functionally related on the molecular level

Elevated APOBEC3B Correlates with Poor Outcomes for Estrogen-Receptor-Positive Breast Cancers

Recent observations connected DNA cytosine deaminase APOBEC3B to the genetic evolution of breast cancer. We addressed whether APOBEC3B is associated with breast cancer clinical outcomes. APOBEC3B messenger RNA (mRNA) levels were related in 1,491 primary breast cancers to disease-free (DFS), metastasis-free (MFS), and overall survival (OS). For independent validation, APOBEC3B mRNA expression was associated with patient outcome data in five additional cohorts (over 3,500 breast cancer cases). In univariate Cox regression analysis, increasing APOBEC3B expression as a continuous variable was associated with worse DFS, MFS, and OS (hazard ratio [HR] = 1.20, 1.21, and 1.24, respectively; all P < .001). Also, in untreated ER-positive (ER+), but not in ER−, lymph-node-negative patients, high APOBEC3B levels were associated with a poor DFS (continuous variable: HR = 1.29, P = .001; dichotomized at the median level, HR = 1.66, P = .0002). This implies that APOBEC3B is a marker of pure prognosis in ER + disease. These findings were confirmed in the analyses of five independent patient sets. In these analyses, APOBEC3B expression dichotomized at the median level was associated with adverse outcomes (METABRIC discovery and validation, 788 and 706 ER + cases, disease-specific survival (DSS), HR = 1.77 and HR = 1.77, respectively, both P < .001; Affymetrix dataset, 754 ER + cases, DFS, HR = 1.57, P = 2.46E-04; NKI295, 181 ER + cases, DFS, HR = 1.72, P = .054; and BIG 1-98, 1,219 ER + cases, breast-cancer-free interval (BCFI), HR = 1.42, P = 0.0079). APOBEC3B is a marker of pure prognosis and poor outcomes for ER + breast cancer, which strongly suggests that genetic aberrations induced by APOBEC3B contribute to breast cancer progression. Electronic supplementary material The online version of this article (doi: 10.1007/s12672-014-0196-8) contains supplementary material, which is available to authorized users