BNDB – The Biochemical Network Database

<p>Abstract</p> <p>Background</p> <p>Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources.</p> <p>Description</p> <p>We present the Biochemical Network Database (BNDB), a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB.</p> <p>Conclusion</p> <p>BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at <url>http://www.bndb.org</url>.</p

An integer linear programming approach for finding deregulated subgraphs in regulatory networks

Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players

BN++- A Biological Information System

Recent years have seen an explosive growth in the amount of biochemical data available. Numerous databases have been established and are being used as an essential resource by biologists around the world. The sheer amount and heterogeneity of these data poses a major challenge: data integration and, based thereupon, the integrative analysis of these data. We present BN++, the biochemical network library, a powerful software package for integrating, analyzing, and visualizing biochemical data in the context of networks. BN++ is based on a comprehensive and extensible object model (BioCore), which has been implemented as a C++ framework, a Java class library, and a relational database. The C++ framework is used to efficiently import, integrate, and analyze the data, which is stored in a data warehouse. The Java-based viewer (BiNA) provides a powerful platform-independent visualization of the data using sophisticated graph layout algorithms. Currently, the data warehouse imports and integrates data from about a dozen important databases including, among others, sequence data, metabolic and regulatory networks, and protein interaction data. We illustrate the usefulness of BN++ with a few select example applications. Availability: BN++ is open source software available from our website at www.bnplusplus. org.

BN++ – A Biological Information System

Recent years have seen an explosive growth in the amount of biochemical data available. Numerous databases have been established and are being used as an essential resource by biologists around the world. The sheer amount and heterogeneity of these data poses a major challenge: data integration and, based thereupon, the integrative analysis of these data. We present BN++, the biochemical network library, a powerful software package for integrating, analyzing, and visualizing biochemical data in the context of networks. BN++ is based on a comprehensive and extensible object model (BioCore), which has been implemented as a C++ framework, a Java class library, and a relational database. The C++ framework is used to efficiently import, integrate, and analyze the data, which is stored in a data warehouse. The Java-based viewer (BiNA) provides a powerful platform-independent visualization of the data using sophisticated graph layout algorithms. Currently, the data warehouse imports and integrates data from about a dozen important databases including, among others, sequence data, metabolic and regulatory networks, and protein interaction data. We illustrate the usefulness of BN++ with a few select example applications

Metabolic pathway representation in BiNA. The KEGG Glycerolipid metabolism of human (on the top) in comparison to the corresponding metabolic representation of BiNA using the KEGG visual style (below).

<p>BiNA's KEGG visual style provides layouts of the pathways which are very similar to the KEGG maps. Additionally, BiNA supports filtering of organism-unspecific parts of a pathway, which improves the readability. In this figure, we manually removed disconnected reactions from BiNA's pathway. Furthermore, neighbored pathways can be directly explored and shown in the same visualization, which clearly supports the biological understanding of relationships across borders of canonical pathways (not shown).</p

Sub-cellular compartment visualization.

<p>The visualization of the KEGG Apoptosis pathway in a layered sub-cellular compartment model demonstrates BiNA possibilities for integrating cellular location information. For this, information, e.g., from SwissProt <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087397#pone.0087397-UniProt1" target="_blank">[29]</a>, can be used to assign the proteins to the layout layers, which correspond to an abstract cell model. This representation is meaningful for highlighting signaling cascades into the nucleus. Since, proteins can have multiple cellular locations, it is also possible to validate the compartment assignment by projecting the ambiguity level of the cellular locations to the node colors: From unambiguous (red) via ambiguous (rose) to white (no information available).</p

BiNA: A Visual Analytics Tool for Biological Network Data

<div><p>Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at <a href="http://bina.unipax.info/" target="_blank">http://bina.unipax.info/</a>.</p></div

Architecture sketch of BiNA and BN++.

<p>BiNA acts as a visualizer for the BN++ data warehouse system, which semantically integrates several biological databases and stores them into BNDB. BiNA is able to access BNDB directly via SQL, either the MySQL or the Apache Derby version. BiNA consists of a number of plug-ins (OSGi bundles), which are packed together for distribution. Using these plug-ins BiNA can import various file formats, use an R server for processing experimental data, and visualize and analyze networks in different contexts. The user is able to extend the functionality of BiNA using the public API of the OSGi bundles.</p

High-throughput data projection in BiNA.

<p>High-throughput datasets can be projected onto a network by simple drag and drop operations. The upper right hand side of the view shows available datasets. When one of these datasets is dragged onto the main network visualization, possible network attributes for projection arise (green boxes). Afterwards, a dialog opens and permits a more detailed configuration of the projection (not shown).</p