VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology

Despite its wide usage in biological databases and applications, the role of the gene ontology (GO) in network analysis is usually limited to functional annotation of genes or gene sets with auxiliary information on correlations ignored. Here, we report on new capabilities of VisANT—an integrative software platform for the visualization, mining, analysis and modeling of the biological networks—which extend the application of GO in network visualization, analysis and inference. The new VisANT functions can be classified into three categories. (i) Visualization: a new tree-based browser allows visualization of GO hierarchies. GO terms can be easily dropped into the network to group genes annotated under the term, thereby integrating the hierarchical ontology with the network. This facilitates multi-scale visualization and analysis. (ii) Flexible annotation schema: in addition to conventional methods for annotating network nodes with the most specific functional descriptions available, VisANT also provides functions to annotate genes at any customized level of abstraction. (iii) Finding over-represented GO terms and expression-enriched GO modules: two new algorithms have been implemented as VisANT plugins. One detects over-represented GO annotations in any given sub-network and the other finds the GO categories that are enriched in a specified phenotype or perturbed dataset. Both algorithms take account of network topology (i.e. correlations between genes based on various sources of evidence). VisANT is freely available at http://visant.bu.edu

VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology

Despite its wide usage in biological databases and applications, the role of the gene ontology (GO) in network analysis is usually limited to functional annotation of genes or gene sets with auxiliary information on correlations ignored. Here, we report on new capabilities of VisANT—an integrative software platform for the visualization, mining, analysis and modeling of the biological networks—which extend the application of GO in network visualization, analysis and inference. The new VisANT functions can be classified into three categories. (i) Visualization: a new tree-based browser allows visualization of GO hierarchies. GO terms can be easily dropped into the network to group genes annotated under the term, thereby integrating the hierarchical ontology with the network. This facilitates multi-scale visualization and analysis. (ii) Flexible annotation schema: in addition to conventional methods for annotating network nodes with the most specific functional descriptions available, VisANT also provides functions to annotate genes at any customized level of abstraction. (iii) Finding over-represented GO terms and expression-enriched GO modules: two new algorithms have been implemented as VisANT plugins. One detects over-represented GO annotations in any given sub-network and the other finds the GO categories that are enriched in a specified phenotype or perturbed dataset. Both algorithms take account of network topology (i.e. correlations between genes based on various sources of evidence). VisANT is freely available at http://visant.bu.edu

CancerLinker: Explorations of Cancer Study Network

Interactive visualization tools are highly desirable to biologist and cancer researchers to explore the complex structures, detect patterns and find out the relationships among bio-molecules responsible for a cancer type. A pathway contains various bio-molecules in different layers of the cell which is responsible for specific cancer type. Researchers are highly interested in understanding the relationships among the proteins of different pathways and furthermore want to know how those proteins are interacting in different pathways for various cancer types. Biologists find it useful to merge the data of different cancer studies in a single network and see the relationships among the different proteins which can help them detect the common proteins in cancer studies and hence reveal the pattern of interactions of those proteins. We introduce the CancerLinker, a visual analytic tool that helps researchers explore cancer study interaction network. Twenty-six cancer studies are merged to explore pathway data and bio-molecules relationships that can provide the answers to some significant questions which are helpful in cancer research. The CancerLinker also helps biologists explore the critical mutated proteins in multiple cancer studies. A bubble graph is constructed to visualize common protein based on its frequency and biological assemblies. Parallel coordinates highlight patterns of patient profiles (obtained from cBioportal by WebAPI services) on different attributes for a specified cancer studyComment: 7 pages, 9 figure

VISIBIOweb: visualization and layout services for BioPAX pathway models

With recent advancements in techniques for cellular data acquisition, information on cellular processes has been increasing at a dramatic rate. Visualization is critical to analyzing and interpreting complex information; representing cellular processes or pathways is no exception. VISIBIOweb is a free, open-source, web-based pathway visualization and layout service for pathway models in BioPAX format. With VISIBIOweb, one can obtain well-laid-out views of pathway models using the standard notation of the Systems Biology Graphical Notation (SBGN), and can embed such views within one's web pages as desired. Pathway views may be navigated using zoom and scroll tools; pathway object properties, including any external database references available in the data, may be inspected interactively. The automatic layout component of VISIBIOweb may also be accessed programmatically from other tools using Hypertext Transfer Protocol (HTTP). The web site is free and open to all users and there is no login requirement. It is available at: http://visibioweb.patika.org

PANDORA: analysis of protein and peptide sets through the hierarchical integration of annotations

Derivation of biological meaning from large sets of proteins or genes is a frequent task in genomic and proteomic studies. Such sets often arise from experimental methods including large-scale gene expression experiments and mass spectrometry (MS) proteomics. Large sets of genes or proteins are also the outcome of computational methods such as BLAST search and homology-based classifications. We have developed the PANDORA web server, which functions as a platform for the advanced biological analysis of sets of genes, proteins, or proteolytic peptides. First, the input set is mapped to a set of corresponding proteins. Then, an analysis of the protein set produces a graph-based hierarchy which highlights intrinsic relations amongst biological subsets, in light of their different annotations from multiple annotation resources. PANDORA integrates a large collection of annotation sources (GO, UniProt Keywords, InterPro, Enzyme, SCOP, CATH, Gene-3D, NCBI taxonomy and more) that comprise ∼200 000 different annotation terms associated with ∼3.2 million sequences from UniProtKB. Statistical enrichment based on a binomial approximation of the hypergeometric distribution and corrected for multiple hypothesis tests is calculated using several background sets, including major gene-expression DNA-chip platforms. Users can also visualize either standard or user-defined binary and quantitative properties alongside the proteins. PANDORA 4.2 is available at http://www.pandora.cs.huji.ac.il

Graphle: Interactive exploration of large, dense graphs

<p>Abstract</p> <p>Background</p> <p>A wide variety of biological data can be modeled as network structures, including experimental results (e.g. protein-protein interactions), computational predictions (e.g. functional interaction networks), or curated structures (e.g. the Gene Ontology). While several tools exist for visualizing large graphs at a global level or small graphs in detail, previous systems have generally not allowed interactive analysis of dense networks containing thousands of vertices at a level of detail useful for biologists. Investigators often wish to explore specific portions of such networks from a detailed, gene-specific perspective, and balancing this requirement with the networks' large size, complex structure, and rich metadata is a substantial computational challenge.</p> <p>Results</p> <p>Graphle is an online interface to large collections of arbitrary undirected, weighted graphs, each possibly containing tens of thousands of vertices (e.g. genes) and hundreds of millions of edges (e.g. interactions). These are stored on a centralized server and accessed efficiently through an interactive Java applet. The Graphle applet allows a user to examine specific portions of a graph, retrieving the relevant neighborhood around a set of query vertices (genes). This neighborhood can then be refined and modified interactively, and the results can be saved either as publication-quality images or as raw data for further analysis. The Graphle web site currently includes several hundred biological networks representing predicted functional relationships from three heterogeneous data integration systems: <it>S. cerevisiae </it>data from bioPIXIE, <it>E. coli </it>data using MEFIT, and <it>H. sapiens </it>data from HEFalMp.</p> <p>Conclusions</p> <p>Graphle serves as a search and visualization engine for biological networks, which can be managed locally (simplifying collaborative data sharing) and investigated remotely. The Graphle framework is freely downloadable and easily installed on new servers, allowing any lab to quickly set up a Graphle site from which their own biological network data can be shared online.</p

The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein–protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org

Comparative interactomics with Funcoup 2.0

FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website