Article

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY 10029, USA.
BMC Systems Biology (Impact Factor: 2.85). 02/2009; 3(1):10. DOI: 10.1186/1752-0509-3-10
Source: PubMed

ABSTRACT Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.
SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names.
SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: http://snavi.googlecode.com. The source code can be accessed from: http://snavi.googlecode.com/svn/trunk.

Full-text

Available from: Avi Ma'ayan, Jun 12, 2015
0 Followers
 · 
91 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Evolutionary simulations can produce datasets consisting of thousands or millions of separate entities, complete with their genealogical relationships. Biologists must examine this data to determine when and where these entities have changed, both on an individual basis and on a population-wide basis. Therefore, desirable features of a visualization system for evolutionary data are the capability of showing the status of the population at any given moment in time, good scalability, and smooth transition between high-level and low-level views. We propose a multi-scale visualization method, including a novel tree layout that both shows population status over time and can easily scale to very large populations. From this layout, the user can navigate to visualizations for moments in time or for individual entities. We demonstrate the effectiveness of the visualization on an existing evolutionary simulation called EVE: Evolution in Variable Environments.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper is concerned with the creation of 'macros' in workflow visualization as a support tool to increase the efficiency of data curation tasks. We propose computation of candidate macros based on their usage in large collections of workflows in data repositories. We describe an efficient algorithm for extracting macro motifs from workflow graphs. We discovered that the state transition information, used to identify macro candidates, characterizes the structural pattern of the macro and can be harnessed as part of the visual design of the corresponding macro glyph. This facilitates partial automation and consistency in glyph design applicable to a large set of macro glyphs. We tested this approach against a repository of biological data holding some 9,670 workflows and found that the algorithmically generated candidate macros are in keeping with domain expert expectations.
    12/2013; 19(12):2576-85. DOI:10.1109/TVCG.2013.225
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time‐varying graphs. Also, in accordance with ever growing amounts of graph‐structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State‐of‐the‐Art Report, we survey available techniques for the visual analysis of large graphs. Our review first considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.
    Computer Graphics Forum 09/2011; 30(6):1719-1749. DOI:10.1111/j.1467-8659.2011.01898.x · 1.60 Impact Factor