On the one hand, to support the communication between Interaction between these two different environments (invoking ImplementationĬyrface is a Java open-source framework developed to establish the connection between Finally, we discuss on-going and future developments. Then, to illustrate the applicability ofĬyrface, and we create a simplified version of theĭataRail 12 workflow to process and visualize experimental data using methods available in This paper is structured as follows: Firstly, we provide a description of the implementation ofĬyrface. RCytoscape is to handle experimental data from Java in the opposite direction of Cyrface: it supports the connection from It is aīioconductor R package that establishes a connection between RCytoscape 9 is another tool that exists to link R and Cytoscape. Thus, Cyrface complements Taverna and Galaxy by enhancing GUIs for R within a different environment with complementary features. Galaxy is an open-source web-platform to assemble workflows based on genomic experimental data analysis. Java open-source tool for the general development and execution of workflows. By linking these two environments,Ĭyrface allows one to use Cytoscape as a user interface for R packages andĬytoscape plug-ins in order to reach the wealth of methods implemented inĨ can call R packages from a graphical user interface (GUI)-based interface. Therefore, we developedĬytoscape that facilitates an interface between anyĬyrface is designed to integrate the major strengths ofĬytoscape environments by providing a general It is arguably one of the most used tools in bioinformatics, and has a variety of plug-ins to solve numerous computational biology problems. R and running packages can be a time consuming task and therefore the use of intuitive graphical interfaces can enhance the usability of the tool.Ĥ is a Java open-source framework with an intuitive graphical interface devoted to the visualization and analysis of networks. ![]() It is an open-source project hosting 671 active and curated software packages as of September 2013.įor those not familiar with computational programming, learning These packages are subject to stringent quality control in terms of functionality and documentation. Furthermore,īioconductor 2 provides a comprehensive collection of packages to analyse biological data developed in Multiple R packages for computational biology and bioinformatics are available in various resources such as the Comprehensive R Archive Network (CRAN). Arguably, one of the most used environments is the statistical programming language The availability of high-throughput experimental data has led to the development of multiple computational methods to analyse these data.
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