The Experts below are selected from a list of 7338 Experts worldwide ranked by ideXlab platform
Trey Ideker - One of the best experts on this subject based on the ideXlab platform.
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network propagation in the Cytoscape cyberinfrastructure
PLOS Computational Biology, 2017Co-Authors: Daniel E Carlin, Barry Demchak, Dexter Pratt, Eric Sage, Trey IdekerAbstract:Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.
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Cytoscape the network visualization tool for genomespace workflows
F1000Research, 2014Co-Authors: Barry Demchak, Trey Ideker, Tim Hull, Michael Reich, Ted Liefeld, Michael Smoot, Jill P MesirovAbstract:Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013.
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a travel guide to Cytoscape plugins
Nature Methods, 2012Co-Authors: Rintaro Saito, Alexander R. Pico, Gary D. Bader, Keiichiro Ono, Johannes Ruscheinski, Pengliang Wang, Michael E Smoot, Samad Lotia, Trey IdekerAbstract:This Perspective discusses the registered and publicly available set of Cytoscape plugins to guide potential users to suitable tools.
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Cytoscape 2 8
Bioinformatics, 2011Co-Authors: Michael Smoot, Keiichiro Ono, Johannes Ruscheinski, Pengliang Wang, Trey IdekerAbstract:Summary: Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features—Custom Node Graphics and Attribute Equations—which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties. Availability and implementation: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from http://Cytoscape.org. Contact: [email protected]
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Cytoscape 2 8 new features for data integration and network
2011Co-Authors: E Smoot, Pengliang Wang, Johannes Ruscheinski, Trey IdekerAbstract:Summary: Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features—Custom Node Graphics and Attribute Equations—which can be used jointly to greatly enhance Cytoscape’s data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties. Availability and implementation: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from http://Cytoscape.org.
Barry Demchak - One of the best experts on this subject based on the ideXlab platform.
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Cytoscape Automation: empowering workflow-based network analysis
Genome Biology, 2019Co-Authors: David Otasek, John H. Morris, Jorge Bouças, Alexander R. Pico, Barry DemchakAbstract:Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.
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network propagation in the Cytoscape cyberinfrastructure
PLOS Computational Biology, 2017Co-Authors: Daniel E Carlin, Barry Demchak, Dexter Pratt, Eric Sage, Trey IdekerAbstract:Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.
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1 #SPRINGMESSAGE("ENUM.${ENUM.CLASS.SIMPLENAME}.${ENUM.NAME()}$!{SUFFIX}") CyREST: Turbocharging Cytoscape Access for External Tools via a RESTful API [version 1; referees: 2 approved]
2015Co-Authors: Polytechnicsergey Nepomnyachiy, Paul Shannon, Keiichiro Ono, Tanja Muetze, Georgi Kolishovski, Barry DemchakAbstract:As bioinformatic workflows become increasingly complex and involve multiple specialized tools, so does the difficulty of reliably reproducing those workflows. Cytoscape is a critical workflow component for executing network visualization, analysis, and publishing tasks, but it can be operated only manually via a point-and-click user interface. Consequently, Cytoscape-oriented tasks are laborious and often error prone, especially with multistep protocols involving many networks. In this paper, we present the new cyREST Cytoscape app and accompanying harmonization libraries. Together, they improve workflow reproducibility and researcher productivity by enabling popular languages (e.g., Python and R, JavaScript, and C#) and tools (e.g., IPython/Jupyter Notebook and RStudio) to directly define and query networks, and perform network analysis, layouts and renderings. We describe cyREST’s API and overall construction, and present Python- and R-based examples that illustrate how Cytoscape can be integrated into large scale data analysis pipelines. cyREST is available in the Cytoscape app stor
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CyREST: Turbocharging Cytoscape Access for External Tools via a RESTful API [v1; ref status: indexed, http://f1000r.es/5ly]
F1000 Research Ltd, 2015Co-Authors: Keiichiro Ono, Paul Shannon, Tanja Muetze, Georgi Kolishovski, Barry DemchakAbstract:As bioinformatic workflows become increasingly complex and involve multiple specialized tools, so does the difficulty of reliably reproducing those workflows. Cytoscape is a critical workflow component for executing network visualization, analysis, and publishing tasks, but it can be operated only manually via a point-and-click user interface. Consequently, Cytoscape-oriented tasks are laborious and often error prone, especially with multistep protocols involving many networks. In this paper, we present the new cyREST Cytoscape app and accompanying harmonization libraries. Together, they improve workflow reproducibility and researcher productivity by enabling popular languages (e.g., Python and R, JavaScript, and C#) and tools (e.g., IPython/Jupyter Notebook and RStudio) to directly define and query networks, and perform network analysis, layouts and renderings. We describe cyREST’s API and overall construction, and present Python- and R-based examples that illustrate how Cytoscape can be integrated into large scale data analysis pipelines. cyREST is available in the Cytoscape app store (http://apps.Cytoscape.org) where it has been downloaded over 1900 times since its release in late 2014
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Cytoscape the network visualization tool for genomespace workflows
F1000Research, 2014Co-Authors: Barry Demchak, Trey Ideker, Tim Hull, Michael Reich, Ted Liefeld, Michael Smoot, Jill P MesirovAbstract:Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013.
Ka Yee Yeung - One of the best experts on this subject based on the ideXlab platform.
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GUIdock: Using Docker containers with a common graphics user interface to address the reproducibility of research
PLoS ONE, 2016Co-Authors: Ling Hong Hung, Daniel Kristiyanto, Sung Bong Lee, Ka Yee YeungAbstract:Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface.
Jan Baumbach - One of the best experts on this subject based on the ideXlab platform.
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efficient algorithms for extracting biological key pathways with global constraints
Genetic and Evolutionary Computation Conference, 2012Co-Authors: Jan Baumbach, Tobias Friedrich, Timo Kotzing, Anton Krohmer, Joachim Muller, Josch K PaulingAbstract:The integrated analysis of data of different types and with various interdependencies is one of the major challenges in computational biology. Recently, we developed KeyPathwayMiner, a method that combines biological networks modeled as graphs with disease-specific genetic expression data gained from a set of cases (patients, cell lines, tissues, etc.). We aimed for finding all maximal connected sub-graphs where all nodes but $K$ are expressed in all cases but at most $L$, i.e. key pathways. Thereby, we combined biological networks with OMICS data, instead of analyzing these data sets in isolation. Here we present an alternative approach that avoids a certain bias towards hub nodes: We now aim for extracting all maximal connected sub-networks where all but at most $K$ nodes are expressed in all cases but in total (!) at most $L$, i.e. accumulated over all cases and all nodes in a solution. We call this strategy GLONE (global node exceptions); the previous problem we call INES (individual node exceptions). Since finding GLONE-components is computationally hard, we developed an Ant Colony Optimization algorithm and implemented it with the KeyPathwayMiner Cytoscape framework as an alternative to the INES algorithms. KeyPathwayMiner 3.0 now offers both the INES and the GLONE algorithms. It is available as plugin from Cytoscape and online at http://keypathwayminer.mpi-inf.mpg.de.
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clustermaker a multi algorithm clustering plugin for Cytoscape
BMC Bioinformatics, 2011Co-Authors: John H. Morris, Gary D. Bader, Tobias Wittkop, Jan Baumbach, Leonard Apeltsin, Aaron M Newman, Thomas E FerrinAbstract:In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.
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Comprehensive cluster analysis with Transitivity Clustering
Nature Protocols, 2011Co-Authors: Tobias Wittkop, Mario Albrecht, Dorothea Emig, Anke Truss, Sebastian Böcker, Jan BaumbachAbstract:Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.
Mario Albrecht - One of the best experts on this subject based on the ideXlab platform.
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Topological analysis and interactive visualization of biological networks and protein structures
Nature Protocols, 2012Co-Authors: Nadezhda T Doncheva, Yassen Assenov, Francisco S Domingues, Mario AlbrechtAbstract:Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.
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Comprehensive cluster analysis with Transitivity Clustering
Nature Protocols, 2011Co-Authors: Tobias Wittkop, Mario Albrecht, Dorothea Emig, Anke Truss, Sebastian Böcker, Jan BaumbachAbstract:Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.
