Benchmarking network-based gene prioritization methods for cerebral small vessel disease

Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene disease associations and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD). We curated protein-gene interactions (PGI) and gene-disease associations (GDA) from databases and assembled PGI networks and disease-gene heterogenous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19,463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality. Choice of algorithms should be determined before applying to specific disease. Current pure network-based gene prioritization algorithms are unlikely to find novel disease-associated genes that are not associated with known ones. The tools for implementing and benchmarking algorithms have been made available and can be generalized for other diseases

A Gene-Phenotype Network for the Laboratory Mouse and Its Implications for Systematic Phenotyping

The laboratory mouse is the pre-eminent model organism for the dissection of human disease pathways. With the advent of a comprehensive panel of gene knockouts, projects to characterise the phenotypes of all knockout lines are being initiated. The range of genotype-phenotype associations can be represented using the Mammalian Phenotype ontology. Using publicly available data annotated with this ontology we have constructed gene and phenotype networks representing these associations. These networks show a scale-free, hierarchical and modular character and community structure. They also exhibit enrichment for gene coexpression, protein-protein interactions and Gene Ontology annotation similarity. Close association between gene communities and some high-level ontology terms suggests that systematic phenotyping can provide a direct insight into underlying pathways. However some phenotypes are distributed more diffusely across gene networks, likely reflecting the pleiotropic roles of many genes. Phenotype communities show a many-to-many relationship to human disease communities, but stronger overlap at more granular levels of description. This may suggest that systematic phenotyping projects should aim for high granularity annotations to maximise their relevance to human disease

Protein localization as a principal feature of the etiology and comorbidity of genetic diseases

Proteins localized within the same subcellular compartment tend to be functionally associated. This study shows that subcellular localization and network distance between disease-associated proteins provide complementary information explaining patterns of disease comorbidity

Protein-protein interactions: network analysis and applications in drug discovery

Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets

Protein–protein interactions and genetic diseases: The interactome

AbstractProtein–protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein–protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function

Structural Bounds on the Dyadic Effect

In this paper we consider the dyadic effect introduced in complex networks when nodes are distinguished by a binary characteristic. Under these circumstances two independent parameters, namely dyadicity and heterophilicity, are able to measure how much the assigned characteristic affects the network topology. All possible configurations can be represented in a phase diagram lying in a two-dimensional space that represents the feasible region of the dyadic effect, which is bound by two upper bounds on dyadicity and heterophilicity. Using some network's structural arguments, we are able to improve such upper bounds and introduce two new lower bounds, providing a reduction of the feasible region of the dyadic effect as well as constraining dyadicity and heterophilicity within a specific range. Some computational experiences show the bounds' effectiveness and their usefulness with regards to different classes of networks

Network biology methods for functional characterization and integrative prioritization of disease genes and proteins

Nowadays, large amounts of experimental data have been produced by high-throughput techniques, in order to provide more insight into complex phenotypes and cellular processes. The development of a variety of computational and, in particular, network-based approaches to analyze these data have already shed light on previously unknown mechanisms. However, we are still far from a comprehensive understanding of human diseases and their causes as well as appropriate preventive measures and successful therapies. This thesis describes the development of methods and user-friendly software tools for the integrative analysis and interactive visualization of biological networks as well as their application to biomedical data for understanding diseases. We design an integrative phenotype-specific framework for prioritizing candidate disease genes and functionally characterizing similar phenotypes. It is applied to the identification of several disease-relevant genes and processes for inflammatory bowel diseases and primary sclerosing cholangitis as well as for Parkinson's disease. Since finding the causative disease genes does often not suffice to understand diseases, we also concentrate on the molecular characterization of sequence mutations and their effect on protein structure and function. We develop a software suite to support the interactive, multi-layered visual analysis of molecular interaction mechanisms such as protein binding, allostery and drug resistance. To capture the dynamic nature of proteins, we also devise an approach to visualizing and analyzing ensembles of protein structures as, for example, generated by molecular dynamics simulations.In den letzten Jahren wurde mittels Hochdurchsatzverfahren eine große Menge experimenteller Daten generiert, um einen Einblick in komplexe Phänotypen und zelluläre Prozesse zu ermöglichen. Die Entwicklung von verschiedenen bioinformatischen und insbesondere netzwerkbasierten Ansätzen zur Analyse dieser Daten konnte bereits Aufschluss über bisher unbekannte Mechanismen geben. Dennoch sind wir weit entfernt von einem umfassenden Verständnis menschlicher Krankheiten und ihrer Ursachen sowie geeigneter präventiver Maßnahmen und erfolgreicher Therapien. Diese Dissertation beschreibt die Entwicklung von Methoden und benutzerfreundlichen Softwarewerkzeugen für die integrative Analyse und interaktive Visualisierung biologischer Netzwerke sowie ihre Anwendung auf biomedizinische Daten zum Verständnis von http://scidok.sulb.uni-saarland.de/volltexte/incoming/2016/6595/Krankheiten. Wir entwerfen ein integratives, phänotypspezifisches Framework für die Priorisierung potentiell krankheitserregender Gene und die funktionelle Charakterisierung ähnlicher Phänotypen. Es wird angewandt, um mehrere krankheitsspezifische Gene und Prozesse von chronisch-entzündlichen Darmerkrankungen und primär sklerosierender Cholangitis sowie von Parkinson zu bestimmen. Da es für das Verständnis von Krankheiten oft nicht genügt, die krankheitserregenden Gene zu entdecken, konzentrieren wir uns auch auf die molekulare Charakterisierung von Sequenzmutationen und ihren Effekt auf die Proteinstruktur und -funktion. Wir entwickeln eine Software, um die interaktive, vielschichtige visuelle Analyse von molekularen Mechanismen wie Proteinfaltung, Allosterie und Arzneimittelresistenz zu unterstützen. Um den dynamischen Charakter von Proteinen zu erfassen, ersinnen wir auch eine Methode für die Visualisierung und Analyse von Proteinstrukturen, welche sich zum Beispiel während Molekulardynamiksimulationen ergeben