This document discusses plans for a major re-architecture of the Cytoscape software platform in version 3.0. The goals are to make it easier for developers to write plugins by adopting modern frameworks like OSGi and Spring, and to support different deployment contexts. This will address current issues like a monolithic architecture that is difficult to extend. The re-architecture aims to improve modularity, enable better plugin interoperability, and facilitate use of Cytoscape in new environments like web applications. Challenges include balancing simplicity of the new API with plugin extensibility.
1. Cytoscape Springs Forward: Re-architecture for Version 3.0 Allan Kuchinsky1,3, Keiichiro Ono2, Michael Smoot2, Trey Ideker2,Annette Adler1 1 Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, CA 95051 USA 2 School of Medicine, University of California, San Diego, CA 92093 USA 3Gladstone Institute of Cardiovascular Disease, San Francisco, CA, 94158-2261, USA email: allan_kuchinsky@agilent.com June, 2009 June 2009
13. June 2009 Page 4 The real biological story is in the relationshipsamongst the biological entities, rather than the entities themselves. Interpretive value comes from integrating diverse measurements within their biological context. “Systems biology is about putting together rather than taking apart, integration rather than reduction..” D. Noble, The Music of life. Biology beyond the genome Oxford University Press 2006. ISBN 0199295735, ISBN 978-0199295739 Roth et al, n engl j med 356;1 www.nejm.org january 4, 2007) show the molecular mechanisms by which certain drugs may induce Valvular Heart Disease.
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15. Currently 75 registered plugins, developed by leading research groups, freely available
16. Community development of plugins strongly encouraged and actively supported by core development team.
17. License: The Cytoscape core distribution is available under GNU Lesser Public License (LGPL). Plugins each have their own licensing policies, most are freely available from the Cytoscape web site.June 2009
34. Prediction of new interactions and functional associations – Statistically significant domain-domain correlations in protein interaction network to predict protein-protein or genetic interactionJune 2009 jActiveModules, UCSD mCode, University of Toronto PathBlast, UCSD DomainGraph, Max Planck Institute
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36. Subnetwork-based diagnosis – source of biomarkers for disease classification, identify interconnected genes whose aggregate expression levels are predictive of disease state
37. Subnetwork-based gene association – map common pathway mechanisms affected by collection of genotypes (SNP, CNV)June 2009 Agilent Literature Search PinnacleZ, UCSD Mondrian, MSKCC
40. Peter Good, PhD, program director for genome informatics in NHGRI’s Division of Extramural Research and program director for the ENCODE (The ENCyclopedia Of DNA Elements) consortium, which is organized by the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health (NIH), which is reporting results of its exhaustive, four-year effort to build a parts list of all biologically functional elements in 1 percent of the human genome). See: http://www.nih.gov/news/pr/jun2007/nhgri-13.htm and http://www.genome.gov/10005107
41. "The Cytoscape project has grown into, probably, the most advanced and well known developer's community in systems biology, and we are very glad to participate in it”
43. “Cytoscape: Hands-Down Winner for Large-Scale Graph Visualization: Where Has the Biology Community Been Hiding This Gem?”
44. by Michael K. Bergman at http://www.mkbergman.com/?p=415). Cytoscape is emerging as the visualization tool of choice for the Semantic Web / RDF community (January 28, 2008)
99. “I need to see an implementation before I can believe it’s feasible” vs. “I can’t evaluate the implementation unless I can see the API/UI for how it will be used”.
107. Core development team: 12 FTE, responsible for core of Cytoscape functionality, funded by Consortium members
108. Core Plugin developers: 10.2 FTE, responsible for extensions that provide common useful functionality, such as layout, editing, web services. Typically funded by Consortium members.
111. User community support through extensive training materials, cased-based tutorials, roving engineer, annual symposiumJune 2009 ISMB Cytoscape Birds-of-a-Feather sessionTuesday 1-2PM, K1 Trey Ideker’s Keynote Address, Tuesday/4:45PM New Challenges and Opportunities in Network Biology
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Gene Function Prediction –basic but important, examining genes (proteins) in a network context shows connections to sets of genes/proteins involved in same biological process that are likely to function in that processDetection of protein complexes/other modular structures – although interaction networks are based on pair-wise interactions, there is clear evidence for modularity & higher order organization (motifs, feedback loops)Network evolution – biological process(s) conservation across species (PathBLAST, NetworkBLAST to align p-p interaction networks & clusters)Prediction of new interactions and functional associations – Statistically significant domain-domain correlations in protein interaction network suggest that certain domain (and domain pairs) mediate protein binding. Machine learning extends this to suggest to predict protein-protein or genetic interaction through integration of diverse types of evidence for interaction
Ties beuatifully into cnvsnp work at agilent.Identification of disease subnetworks– identification of disease network subnetworks that are transcriptionally active in disease. These suggest key pathway components in disease progression and provide leads for further study and potential therapeutic targetsSubnetwork-based diagnosis – subnetworks also provide a rich source of biomarkers for disease classification, based on mRNA profiling integrated with protein networks to identify subnetwork biomarkers (interconnected genes whose aggregate expression levels are predictive of disease state)Subnetwork-based gene association – molecular networks will provide a powerful framework for mapping common pathway mechanisms affected by collection of genotypes (SNP, CNV)