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de novo Generation, Visualization, and Analysis of Biological Networks using Cytoscape and Agilent Literature Search__Allan Kuchinsky__Agilent Technologies, Santa Clara, California, USAAnalyzing high-throughput experimental data in the context of biological processes can be a daunting task. For example, analysis of microarray data is useful only in identifying statistically significant gene expression changes, whereas identifying discriminatory pathways/networks of gene interactions from a set of significant molecules can provide critical information for understanding complex processes and identifying therapeutic targets from among the potentially large list of differentially expressed genes. I will present a systems-based method to analyze high-throughput data for studying complex diseases, based upon literature-based de novo network construction, supplemented by visualization techniques for examining generated networks against experimental data. Central to our approach are two software systems:• Cytoscape, an open source bioinformatics software platform for visualizingmolecular interaction networks and integratingthese interactions with gene expression profiles and other state data. ([http://www.cytoscape.org/ www.cytoscape.org]). • Agilent Literature Search, a meta-search tool for automatically querying multiple text-based search engines. Computationally extracted associations are grouped into a network that is viewed and manipulated in Cytoscape. Available as a Cytoscape plugin. I will provide an in-depth look at Cytoscape and Agilent Literature Search and discusses their application in collaborative studies with researchers at the Stanford University School of Medicine in the area of cardiovascular disease. Data keywords: gene expression, scientific text, unstructured data sources, increased resolution of data analysisCytoscape keywords: connectivity analysis, data overlay, multi-datapoint visualization (heatstrips). |
= R2 and Cytoscape: Graph based integration and analysis of molecular biological data = '''Piet Molenaar''' ''Academic Medical Center, University of Amsterdam, the Netherlands'' Cytoscape provides a convenient way of annotating your network of choice with micro array data through an id-mapping mechanism provided by the BioMartPlugin. In this demo I’ll show an example of that. Probeset validation and statistical analysis however, are lacking from this type of analysis workflow. Driven by questions from molecular biologists we therefore set out to develop R2, a web-based array data analysis platform (humangenetics-amc.nl). Through a webstart based integration with Cytoscape, the results of this data analysis can be visualized as a network. This provides access to a wealth of plugins to further functionally enrich your network and derive new biologically testable hypotheses. In that context I will demo the Gene Ontology plugin BiNGO, the cluster detection tool MCODE and the network inferring tool CytoProphet. They will be used for analysis of our timeseries experiments where the response to gene-silencing through siRna was measured in Neuroblastoma cell-lines. Focus will be on the transcription factor backbone network. '''__Data keywords__''': Gene expression, Transcription factors, Probeset validation, Time series '''__Cytoscape keywords__''': Network and Attribute I/O, Network Inference, Functional Enrichment |
R2 and Cytoscape: Graph based integration and analysis of molecular biological data
Piet Molenaar
Academic Medical Center, University of Amsterdam, the Netherlands
Cytoscape provides a convenient way of annotating your network of choice with micro array data through an id-mapping mechanism provided by the BioMartPlugin. In this demo I’ll show an example of that.
Probeset validation and statistical analysis however, are lacking from this type of analysis workflow. Driven by questions from molecular biologists we therefore set out to develop R2, a web-based array data analysis platform (humangenetics-amc.nl). Through a webstart based integration with Cytoscape, the results of this data analysis can be visualized as a network.
This provides access to a wealth of plugins to further functionally enrich your network and derive new biologically testable hypotheses. In that context I will demo the Gene Ontology plugin BiNGO, the cluster detection tool MCODE and the network inferring tool CytoProphet. They will be used for analysis of our timeseries experiments where the response to gene-silencing through siRna was measured in Neuroblastoma cell-lines. Focus will be on the transcription factor backbone network.
Data keywords: Gene expression, Transcription factors, Probeset validation, Time series
Cytoscape keywords: Network and Attribute I/O, Network Inference, Functional Enrichment