Literature DB >> 12620107

Osprey: a network visualization system.

Bobby-Joe Breitkreutz1, Chris Stark, Mike Tyers.   

Abstract

We have developed a software platform called Osprey for visualization and manipulation of complex interaction networks. Osprey builds data-rich graphical representations that are color-coded for gene function and experimental interaction data. Mouse-over functions allow rapid elaboration and organization of network diagrams in a spoke model format. User-defined large-scale datasets can be readily combined with Osprey for comparison of different methods.

Entities:  

Mesh:

Year:  2003        PMID: 12620107      PMCID: PMC153462          DOI: 10.1186/gb-2003-4-3-r22

Source DB:  PubMed          Journal:  Genome Biol        ISSN: 1474-7596            Impact factor:   13.583


Rationale

The rapidly expanding biological datasets of physical, genetic and functional interactions present a daunting task for data visualization and evaluation [1]. Existing applications such as Pajek allow the user to visualize networks in a simple graphical format [2], but lack the necessary features needed for functional assessment and comparative analysis between datasets. Typically, interaction networks are viewed within a graphing application, but data is manipulated in other contexts, often manually. To address these shortfalls, we developed a network visualization system called Osprey that not only represents interactions in a flexible and rapidly expandable graphical format, but also provides options for functional comparisons between datasets. Osprey was developed with the Sun Microsystems Java Standard Development Kit version 1.4.0_02 [3], which allows it to be used both in stand-alone form and as an add-on viewer for online interaction databases.

Network visualization

Osprey represents genes as nodes and interactions as edges between nodes (Figure 1). Unlike other applications, Osprey is fully customizable and allows the user to define personal settings for generation of interaction networks, as described below. Any interaction dataset can be loaded into Osprey using one of several standard file formats, or by upload from an underlying interaction database. By default, Osprey uses the General Repository for Interaction Datasets as a database (The GRID [4]), from which the user can rapidly build out interaction networks. User-defined interactions are added or subtracted from mouse-over pop-up windows that link to the database. Networks can be saved as tab-delimited text files for future manipulation or exported as JPEG or JPG graphics, portable network graphics (PNG), and scalable vector graphics (SVG) [5]. The SVG image format allows the user to produce high-quality images that can be opened in applications such as Adobe Illustrator [6] for further manipulation.
Figure 1

Representative Osprey network with genes colored by GO process and interactions colored by experimental system.

Searches and filters

A drawback of current network visualization systems is the inability to search the network for an individual gene in the context of large graphs. To overcome this problem, Osprey allows text-search queries by gene names. A further difficulty with visualization systems is the absence of functional information within the graphical interface. This problem is remedied by Osprey, which provides a one-click link to all database fields for all displayed nodes including open reading frame (ORF) name, gene aliases, and a description of gene function. By default, this information is obtained from The GRID, which in turn compiles gene annotations provided by the Saccharomyces Genome Database (SGD [7]). Various filters have been developed that allow the user to query the network. For example, an interaction network can be parsed for interactions derived from a particular experimental method. Current Osprey filters include source, function, experimental system and connectivity (Figure 2).
Figure 2

Searches and filters. (a) Network containing 2,245 vertices and 6,426 edges from combined datasets of Gavin et al. [10], shown in red, and Ho et al. [11], shown in yellow. (b) A source filter reveals only those interactions shared by both datasets, namely 212 vertices and 188 edges.

Network layout

As network complexity increases, graphical representations become cluttered and difficult to interpret. Osprey simplifies network layouts through user-implemented node relaxation, which disperses nodes and edges according to any one of a number of layout options. Any given node or set of nodes can be locked into place in order to anchor the network. Osprey also provides several default network layouts, including circular, concentric circles, spoke and dual ring (Figure 3). Finally, for comparison of large-scale datasets, Osprey can superimpose two or more datasets on top of each other in an additive manner. In conjunction with filter options, this feature allows interactions specific to any given approach to be identified.
Figure 3

Layout options in Osprey. (a) Circular; (b) concentric circle with five rings; (c) dual ring with highly connected nodes on the inside; (d) dual ring with highly connected nodes outside; (e) spoked dual ring.

Color representations

Osprey allows user defined colors to indicate gene function, experimental systems and data sources. Genes are colored by their biological process as defined by standardized Gene Ontology (GO) annotations. Genes that have been assigned more than one process are represented as multicolored pie charts. Osprey currently recognizes 29 biological processes derived from the categories maintained by the GO Consortium [8]. Interactions are colored by experimental system along the entire length of the edge between two nodes. If a given interaction is supported by multiple experimental systems, the edges are segmented into multiple colors to reflect each system. Alternatively, interactions can be colored by data source, again as multiply colored if more than one source supports the interaction. When combined with filter options, a network can be rapidly visualized according to any number of experimental parameters.

Osprey download

A personal copy of the Osprey network visualization system version 0.9.9 for use in not-for-profit organizations can be downloaded from the Osprey webpage at [9]. Registration is required for the sole purpose of enabling notification of software fixes and updates. A limited version of Osprey used for online interaction viewing can be used at The GRID website [4]. For implementation of Osprey as an online viewer for other online interaction databases please contact the authors.
  4 in total

1.  Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

Authors:  M Ashburner; C A Ball; J A Blake; D Botstein; H Butler; J M Cherry; A P Davis; K Dolinski; S S Dwight; J T Eppig; M A Harris; D P Hill; L Issel-Tarver; A Kasarskis; S Lewis; J C Matese; J E Richardson; M Ringwald; G M Rubin; G Sherlock
Journal:  Nat Genet       Date:  2000-05       Impact factor: 38.330

Review 2.  A biological atlas of functional maps.

Authors:  M Vidal
Journal:  Cell       Date:  2001-02-09       Impact factor: 41.582

3.  Functional organization of the yeast proteome by systematic analysis of protein complexes.

Authors:  Anne-Claude Gavin; Markus Bösche; Roland Krause; Paola Grandi; Martina Marzioch; Andreas Bauer; Jörg Schultz; Jens M Rick; Anne-Marie Michon; Cristina-Maria Cruciat; Marita Remor; Christian Höfert; Malgorzata Schelder; Miro Brajenovic; Heinz Ruffner; Alejandro Merino; Karin Klein; Manuela Hudak; David Dickson; Tatjana Rudi; Volker Gnau; Angela Bauch; Sonja Bastuck; Bettina Huhse; Christina Leutwein; Marie-Anne Heurtier; Richard R Copley; Angela Edelmann; Erich Querfurth; Vladimir Rybin; Gerard Drewes; Manfred Raida; Tewis Bouwmeester; Peer Bork; Bertrand Seraphin; Bernhard Kuster; Gitte Neubauer; Giulio Superti-Furga
Journal:  Nature       Date:  2002-01-10       Impact factor: 49.962

4.  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

Authors:  Yuen Ho; Albrecht Gruhler; Adrian Heilbut; Gary D Bader; Lynda Moore; Sally-Lin Adams; Anna Millar; Paul Taylor; Keiryn Bennett; Kelly Boutilier; Lingyun Yang; Cheryl Wolting; Ian Donaldson; Søren Schandorff; Juanita Shewnarane; Mai Vo; Joanne Taggart; Marilyn Goudreault; Brenda Muskat; Cris Alfarano; Danielle Dewar; Zhen Lin; Katerina Michalickova; Andrew R Willems; Holly Sassi; Peter A Nielsen; Karina J Rasmussen; Jens R Andersen; Lene E Johansen; Lykke H Hansen; Hans Jespersen; Alexandre Podtelejnikov; Eva Nielsen; Janne Crawford; Vibeke Poulsen; Birgitte D Sørensen; Jesper Matthiesen; Ronald C Hendrickson; Frank Gleeson; Tony Pawson; Michael F Moran; Daniel Durocher; Matthias Mann; Christopher W V Hogue; Daniel Figeys; Mike Tyers
Journal:  Nature       Date:  2002-01-10       Impact factor: 49.962

  4 in total
  156 in total

1.  Exploration of biological network centralities with CentiBiN.

Authors:  Björn H Junker; Dirk Koschützki; Falk Schreiber
Journal:  BMC Bioinformatics       Date:  2006-04-21       Impact factor: 3.169

2.  Computational approaches to protein-protein interaction.

Authors:  Giacomo Franzot; Oliviero Carugo
Journal:  J Struct Funct Genomics       Date:  2003

3.  A genome-wide telomere screen in yeast: the long and short of it all.

Authors:  Dawn Edmonds; Bobby-Joe Breitkreutz; Lea Harrington
Journal:  Proc Natl Acad Sci U S A       Date:  2004-06-22       Impact factor: 11.205

Review 4.  Charting gene regulatory networks: strategies, challenges and perspectives.

Authors:  Gong-Hong Wei; De-Pei Liu; Chih-Chuan Liang
Journal:  Biochem J       Date:  2004-07-01       Impact factor: 3.857

5.  Identifying gene interaction networks.

Authors:  Gurkan Bebek
Journal:  Methods Mol Biol       Date:  2012

6.  Diverse functions of spindle assembly checkpoint genes in Saccharomyces cerevisiae.

Authors:  Jewel A Daniel; Brice E Keyes; Yvonne P Y Ng; C Onyi Freeman; Daniel J Burke
Journal:  Genetics       Date:  2005-09-12       Impact factor: 4.562

7.  The GTPase-activating enzyme Gyp1p is required for recycling of internalized membrane material by inactivation of the Rab/Ypt GTPase Ypt1p.

Authors:  Céline Lafourcade; Jean-Marc Galan; Yvonne Gloor; Rosine Haguenauer-Tsapis; Matthias Peter
Journal:  Mol Cell Biol       Date:  2004-05       Impact factor: 4.272

8.  VirtualPlant: a software platform to support systems biology research.

Authors:  Manpreet S Katari; Steve D Nowicki; Felipe F Aceituno; Damion Nero; Jonathan Kelfer; Lee Parnell Thompson; Juan M Cabello; Rebecca S Davidson; Arthur P Goldberg; Dennis E Shasha; Gloria M Coruzzi; Rodrigo A Gutiérrez
Journal:  Plant Physiol       Date:  2009-12-09       Impact factor: 8.340

9.  Graphle: Interactive exploration of large, dense graphs.

Authors:  Curtis Huttenhower; Sajid O Mehmood; Olga G Troyanskaya
Journal:  BMC Bioinformatics       Date:  2009-12-14       Impact factor: 3.169

10.  Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.

Authors:  Pei-Ming Song; Yang Zhang; Yu-Fei He; Hui-Min Bao; Jian-Hua Luo; Yin-Kun Liu; Peng-Yuan Yang; Xian Chen
Journal:  World J Gastroenterol       Date:  2008-10-14       Impact factor: 5.742

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.