Summary: Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features—Custom Node Graphics and Attribute Equations—which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties.Availability and implementation: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from http://cytoscape.org.Contact: msmoot@ucsd.edu

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.

Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5–2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.

Primary cilia are evolutionarily conserved cellular organelles that organize diverse signaling pathways1,2. Defects in the formation or function of primary cilia are associated with a spectrum of human diseases and developmental abnormalities3. Genetic screens in model organisms have discovered core machineries of cilium assembly and maintenance4. However, regulatory molecules that coordinate the biogenesis of primary cilia with other cellular processes, including cytoskeletal organization, vesicle trafficking and cell-cell adhesion, remain to be identified. Here we report the results of a functional genomic screen using RNA interference (RNAi) to identify human genes involved in ciliogenesis control. The screen identified 36 positive and 13 negative ciliogenesis modulators, which include molecules involved in actin dynamics and vesicle trafficking. Further investigation demonstrated that blocking actin assembly facilitates ciliogenesis by stabilizing the pericentrosomal preciliary compartment (PPC), a previously uncharacterized compact vesiculotubular structure storing transmembrane proteins destined for cilia during the early phase of ciliogenesis. PPC was labeled by recycling endosome markers. Moreover, knockdown of modulators that are involved in the endocytic recycling pathway affected the formation of PPC as well as ciliogenesis. Our results uncover a critical regulatory step that couples actin dynamics and endocytic recycling with ciliogenesis, and also provide potential target molecules for future study.

Although artificial neural networks simulate a variety of human functions, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) which couple the model’s inner workings to those of real systems. Here we develop DCell, a VNN embedded in the hierarchical structure of 2526 subsystems comprising a eukaryotic cell (http://d-cell.ucsd.edu/). Trained on several million genotypes, DCell simulates cellular growth nearly as accurately as laboratory observations. During simulation, genotypes induce patterns of subsystem activities, enabling in-silico investigations of the molecular mechanisms underlying genotype-phenotype associations. These mechanisms can be validated and many are unexpected; some are governed by Boolean logic. Cumulatively, 80% of the importance for growth prediction is captured by 484 subsystems (21%), reflecting the emergence of a complex phenotype. DCell provides a foundation for decoding the genetics of disease, drug resistance, and synthetic life.