Integrated web visualizations for protein-protein interaction databases

BACKGROUND: Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. RESULTS: We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. CONCLUSIONS: Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing

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

JNets: Exploring networks by integrating annotation

<p>Abstract</p> <p>Background</p> <p>A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.</p> <p>Results</p> <p>JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response.</p> <p>Conclusion</p> <p>JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at <url>http://www.manchester.ac.uk/bioinformatics/jnets</url>.</p

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

JNets: Exploring networks by integrating annotation

<p>Abstract</p> <p>Background</p> <p>A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.</p> <p>Results</p> <p>JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response.</p> <p>Conclusion</p> <p>JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at <url>http://www.manchester.ac.uk/bioinformatics/jnets</url>.</p

Structure motivator: a tool for exploring small three-dimensional elements in proteins

&lt;br&gt;Background: Protein structures incorporate characteristic three-dimensional elements defined by some or all of hydrogen bonding, dihedral angles and amino acid sequence. The software application, Structure Motivator, allows interactive exploration and analysis of such elements, and their resolution into sub-classes.&lt;/br&gt; &lt;br&gt;Results: Structure Motivator is a standalone application with an embedded relational database of proteins that, as a starting point, can furnish the user with a palette of unclassified small peptides or a choice of pre-classified structural motifs. Alternatively the application accepts files of data generated externally. After loading, the structural elements are displayed as two-dimensional plots of dihedral angles (φ/ψ, φ/χ1 or in combination) for each residue, with visualization options to allow the conformation or amino acid composition at one residue to be viewed in the context of that at other residues. Interactive selections may then be made and structural subsets saved to file for further sub-classification or external analysis. The application has been applied both to classical motifs, such as the β-turn, and ‘non-motif’ structural elements, such as specific segments of helices.&lt;/br&gt; &lt;br&gt;Conclusions: Structure Motivator allows structural biologists, whether or not they possess computational skills, to subject small structural elements in proteins to rapid interactive analysis that would otherwise require complex programming or database queries. Within a broad group of structural motifs, it facilitates the identification and separation of sub-classes with distinct stereochemical properties.&lt;/br&gt

VisANT: an online visualization and analysis tool for biological interaction data

BACKGROUND: New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. RESULTS: We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology. CONCLUSION: VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at

CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks

Baumbach J, Brinkrolf K, Czaja LF, Rahmann S, Tauch A. CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. BMC Genomics. 2006;7(1): 24