enhancedGraphics (http://apps.cytoscape.org/apps/enhancedGraphics) is a Cytoscape app that implements a series of enhanced charts and graphics that may be added to Cytoscape nodes. It enables users and other app developers to create pie, line, bar, and circle plots that are driven by columns in the Cytoscape Node Table. Charts are drawn using vector graphics to allow full-resolution scaling.

enhancedGraphics ( http://apps.cytoscape.org/apps/enhancedGraphics) is a Cytoscape app that implements a series of enhanced charts and graphics that may be added to Cytoscape nodes. It enables users and other app developers to create pie, line, bar, and circle plots that are driven by columns in the Cytoscape Node Table. Charts are drawn using vector graphics to allow full-resolution scaling.

The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.

Angioplasty and stenting is the primary treatment for flow-limiting atherosclerosis; however, this strategy is limited by pathological vascular remodeling. Using a systems approach, we identified a role for the network hub gene glutathione peroxidase-1 (GPX1) in pathological remodeling following human blood vessel stenting. Constitutive deletion of Gpx1 in atherosclerotic mice recapitulated this phenotype of increased vascular smooth muscle cell (VSMC) proliferation and plaque formation. In an independent patient cohort, gene variant pair analysis identified an interaction of GPX1 with the orphan protooncogene receptor tyrosine kinase ROS1. A meta-analysis of the only genome-wide association studies of human neointima-induced in-stent stenosis confirmed the association of the ROS1 variant with pathological remodeling. Decreased GPX1 expression in atherosclerotic mice led to reductive stress via a time-dependent increase in glutathione, corresponding to phosphorylation of the ROS1 kinase activation site Y2274. Loss of GPX1 function was associated with both oxidative and reductive stress, the latter driving ROS1 activity via s-glutathiolation of critical residues of the ROS1 tyrosine phosphatase SHP-2. ROS1 inhibition with crizotinib and deglutathiolation of SHP-2 abolished GPX1-mediated increases in VSMC proliferation while leaving endothelialization intact. Our results indicate that GPX1-dependent alterations in oxido-reductive stress promote ROS1 activation and mediate vascular remodeling.

The Synthetic Biology Open Language (SBOL) is a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-adopted, formalized format for exchange between software tools, research groups, and commercial service providers. The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. As a community-driven standard, SBOL adapts as synthetic biology evolves, providing specific capabilities for different aspects of the synthetic biology workflow. The SBOL Developers Group has implemented SBOL 1.1 as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. This paper also reports on early successes, including a demonstration of the utility of SBOL for information exchange between three different tools from three academic sites.

Chris Evelo Head of Department of Bioinformatics, BiGCaT at Maastricht University, The Netherlands Hiroaki Kitano Director of Systems Biology Institute in Tokyo; Professor, Okinawa Institute of Science and Technology Graduate University, Japan Chris Sander Chair of Computational Biology at Sloan-Kettering Institute, New York, New York, USA Josh Stuart Assoc. Professor of Biomedical Engineering at UC Santa Cruz, USA

Open Language (SBOL) Version 1.1.0 to enable the electronic exchange of information describing DNA components used in synthetic biology. We define: 1. the vocabulary, a set of preferred terms and 2. the core data model, a common computational representation.

Summary: We present a Cytoscape plugin called Mosaic to support interactive network annotation, partitioning, layout and coloring based on gene ontology or other relevant annotations. Availability: Mosaic is distributed for free under the Apache v2.0 open source license and can be downloaded via the Cytoscape plugin manager. A detailed user manual is available on the Mosaic web site (http://nrnb.org/tools/mosaic). Contact: apico@gladstone.ucsf.edu

Open Language (SBOL) Version 1.0.0 to enable the electronic exchange of information describing DNA components used in synthetic biology. We define: 1. the vocabulary, a set of preferred terms and 2. the core data model, a common computational representation.

1. PurposeIn this BioBricks Foundation Request for Comments (BBF RFC), we specify the Synthetic Biology Open Language (SBOL) Version 1.0. 0 to enable the electronic exchange of information describing DNA components used in synthetic biology. We define: 1. the vocabulary, a set of preferred terms and 2. the core data model, a common computational representation.

Background Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease. Results We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets. Conclusion We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources.

Summary: GLay provides Cytoscape users an assorted collection of versatile community structure algorithms and graph layout functions for network clustering and structured visualization. High performance is achieved by dynamically linking highly optimized C functions to the Cytoscape JAVA program, which makes GLay especially suitable for decomposition, display and exploratory analysis of large biological networks. Availability: http://brainarray.mbni.med.umich.edu/glay/ Contact: sugang@umich.edu

The role of alternative splicing in self-renewal, pluripotency and tissue lineage specification of human embryonic stem cells (hESCs) is largely unknown. To better define these regulatory cues, we modified the H9 hESC line to allow selection of pluripotent hESCs by neomycin resistance and cardiac progenitors by puromycin resistance. Exon-level microarray expression data from undifferentiated hESCs and cardiac and neural precursors were used to identify splice isoforms with cardiac-restricted or common cardiac/neural differentiation expression patterns. Splice events for these groups corresponded to the pathways of cytoskeletal remodeling, RNA splicing, muscle specification, and cell cycle checkpoint control as well as genes with serine/threonine kinase and helicase activity. Using a new program named AltAnalyze (http://www.AltAnalyze.org), we identified novel changes in protein domain and microRNA binding site architecture that were predicted to affect protein function and expression. These included an enrichment of splice isoforms that oppose cell-cycle arrest in hESCs and that promote calcium signaling and cardiac development in cardiac precursors. By combining genome-wide predictions of alternative splicing with new functional annotations, our data suggest potential mechanisms that may influence lineage commitment and hESC maintenance at the level of specific splice isoforms and microRNA regulation.

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. As a key feature, Cytoscape enables biologists to determine and analyze the interconnectivity of a list of genes or proteins. This unit explains how to use Cytoscape to load and navigate biological network information and view mRNA expression profiles and other functional genomics and proteomics data in the context of the network obtained for genes of interest. Additional analyses that can be performed with Cytoscape are also discussed. Curr. Protoc. Bioinform . 23:8.13.1‐8.13.20. © 2008 by John Wiley & Sons, Inc.

Summary: Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/ Contact: ashkenaz@agri. huji. ac. il

The search for DNA alterations that cause human disease has been an area of active research for more than 50 years, since the time that the genetic code was first solved. In the absence of data implicating chromosomal aberrations, researchers historically have performed whole genome linkage analysis or candidate gene association analysis to develop hypotheses about the genes that most likely cause a specific phenotype or disease. Whereas whole genome linkage analysis examines all chromosomal locations without a priori predictions regarding what genes underlie susceptibility, candidate gene association studies require a researcher to know in advance the genes that he or she wishes to test (based on their knowledge of a disease). To date, very few whole genome linkage studies and candidate gene studies have produced results that lead to generalizable findings about common diseases. One factor contributing to this lack of results has certainly been the previously limited resolution of the techniques. Recent technological advances, however, have made it possible to perform highly informative whole genome linkage and association analyses, as well as whole genome transcription (transcriptome) analysis. In addition, for the first time we can detect structural DNA aberrations throughout the genome on a fine scale. Each of these four approaches has its own strengths and weaknesses, but taken together, the results from an integrated analysis can implicate highly promising novel candidate genes. Here, we provide an overview of the integrated methodology that we have used to combine high-throughput genetic and functional genomic data with bioinformatics data that have produced new insights into the potential biological basis for schizophrenia. We believe that the potential of this combined approach is greater than that of a single mode of discovery, particularly for complex genetic diseases.