ENViz (Enrichment Analysis and Visualization) is a Cytoscape app that performs joint enrichment analysis of two types of sample matched datasets in the context of systematic annotations. Such datasets may be gene expression or any other high-throughput data collected in the same set of samples. The enrichment analysis is done in the context of pathway information, gene ontology or any custom annotation of the data. The results of the analysis consist of significant associations between profiled elements of one of the datasets to the annotation terms (e.g. miR-19 was associated to the cell-cycle process in breast cancer samples). The results of the enrichment analysis are visualized as an interactive Cytoscape network.

Summary: VistaClara is a plug-in for Cytoscape which provides a more flexible means to visualize gene and protein expression within a network context. An extended attribute browser is provided in the form of a graphical and interactive permutation matrix that resembles the heat map displays popular in gene-expression analysis. This extended browser permits a variety of display options and interactions not currently available in Cytoscape. Availability: http://chianti.ucsd.edu/cyto_web/plugins/index.php Contact: robert_kincaid@agilent.com

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation.