Computational Tools for the Interactive Exploration of Proteomic and Structural Data

Morris, John H.; Meng, Elaine C.

doi:10.1074/mcp.r000007-mcp201

Cited by 12 publications

(12 citation statements)

References 75 publications

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“…Using the mean distances, a promoter-enhancer interaction network was built as a weighted undirected graph, in which each node represents either a promoter or an enhancer in a specific locus and each edge is weighted by its mean distances values. The promoter-enhancer interaction network was decomposed into communities using MCODE clustering algorithm as implemented in clusterMaker2 64 and visualized with Cytoscape 3.5 65 .…”

Section: Generation Of 3d Ensemble Structures Of Enhancer Hubs Relatmentioning

confidence: 99%

Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes

Miguel-Escalada

Bonàs-Guarch

Cebola

et al. 2018

Preprint

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Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer regions (enhancer clusters, stretch enhancers or super-enhancers). So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in 3Dspace. Furthermore, their target genes are generally unknown. We have now created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers with their target genes, often located hundreds of kilobases away. It further revealed sets of islet enhancers, superenhancers and active promoters that form 3D higher-order hubs, some of which show coordinated glucose-dependent activity. Hub genetic variants impact the heritability of insulin secretion, and help identify individuals in whom genetic variation of islet function is important for T2D. Human islet 3D chromatin architecture thus provides a framework for interpretation of T2D GWAS signals. 45. View ORCID ProfileMark I McCarthy. Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. BioRxiv (2018).

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Section: Generation Of 3d Ensemble Structures Of Enhancer Hubs Relatmentioning

confidence: 99%

Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes

Miguel-Escalada

Bonàs-Guarch

Cebola

et al. 2018

Preprint

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“…For instance, plug-ins such as ClusterMaker 49 , GLay 50 , MCODE 51 , MINE 52 and NeMo 53 can be used to identify and visualize clusters in networks, whereas the DomainGraph plug-in has a special focus on the visual analysis of the effect of alternative splicing on gene and protein networks 54 . Another Cytoscape plug-in with functionality related to NetworkAnalyzer and RINalyzer is CentiScaPe, which also computes network centrality measures 55 .…”

Section: Introductionmentioning

confidence: 99%

Topological analysis and interactive visualization of biological networks and protein structures

et al. 2012

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PROTOCOL 670 | VOL.7 NO.4 | 2012 | NATURE PROTOCOLS INTRODUCTION Current high-throughput techniques such as yeast two-hybrid screens for protein interaction partners produce great volumes of experimental data that can be integrated and explored to gain insight into biological processes performed by interacting molecules 1-6. Furthermore, structural biologists study the interactions of residues in protein structures to understand complex protein structure-function relationships 7-11. Commonly, large-scale interaction data are represented as networks and initially analyzed by graph-theoretic methods to characterize the topological network structure and its global and local interaction properties 12-18. A number of software tools are available for the visual exploration and computational analysis of networks 19-21. General software libraries for network analysis are the Java framework JUNG 22 , the C + + library LEDA 23 , the Python package NetworkX 24 and R packages such as igraph 25 , statnet 26 , sna 27 , tnet 28 and QuACN 29. However, they cannot be applied by users without programming expertise. In contrast, sophisticated free software platforms such as Pajek 30 , VisANT 31 , ONDEX 32 and BIANA 33 provide graphical user interfaces for the analysis of biological networks. In addition, the free and stand-alone Cytoscape platform has gained considerable interest in recent years because of its open-source code development and its rapidly growing community of users and developers 34,35. In particular, its functionality is easily extendable by additional plug-ins that support specific network analysis tasks. For example, software protocols are already available for cluster analysis with the TransClust and ClusterExplorer plug-ins 36 , as well as for the integration of physical and genetic interactions into module maps with the PanGIA plug-in 37. Here we demonstrate how to apply two of our Cytoscape plug-ins, NetworkAnalyzer 38 and RINalyzer 39 , for the standard and advanced analysis of network topologies. NetworkAnalyzer performs a comprehensive analysis of network topologies without requiring advanced knowledge in graph theory or programming expertise 38. In particular, it supports the characterization of molecular networks in terms of scale-free and small-world properties, modularity and hierarchical structure 5,12,13,40 , the identification of important network nodes and edges based on topological parameters 11,41-43 , and the comparison of networks with regard to their topology 44-47. Since its initial release in 2007, NetworkAnalyzer has been extended by additional features and topological parameters and is widely used in academia and industry as indicated by thousands of software downloads. Recently, this plug-in became an integral part of each standard installation of Cytoscape, and its source code was published under the GNU Lesser General Public License. Basically, NetworkAnalyzer efficiently computes a number of topological parameters, including node degree, clustering and topological coefficient, charact...

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“…This process can of course generally be carried out in much less time than it would take to do an experiment while the underlying HT data are becoming cheaper and cheaper to create. There is a considerable literature dedicated to familiarizing a beginner with the available databases and tools both for analyzing biological networks (33, 38, 100, 132) and network pharmacology (105). Much effort has also been put into the development and maintenance of software suites and web-servers that integrate and apply the various tools that are available (48, 56, 64).…”

Section: Tools and Resources For Network Biologymentioning

confidence: 99%

Structural Bioinformatics of the Interactome

Petrey

2014

Annu. Rev. Biophys.

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The last decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information, and to analyze this information so as to infer both the function of individual molecules and how they interact to modulate the behavior of biological systems. Here we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance the basic understanding of biological systems and their disregulation, and how they are being applied in drug development.

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Computational Tools for the Interactive Exploration of Proteomic and Structural Data

Cited by 12 publications

References 75 publications

Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes

Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes

Topological analysis and interactive visualization of biological networks and protein structures

Structural Bioinformatics of the Interactome

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