BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology.

BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole

BiologicalNetworks: visualization and analysis tool for systems biology

Systems level investigation of genomic scale information requires the development of truly integrated databases dealing with heterogeneous data, which can be queried for simple properties of genes or other database objects as well as for complex network level properties, for the analysis and modelling of complex biological processes. Towards that goal, we recently constructed PathSys, a data integration platform for systems biology, which provides dynamic integration over a diverse set of databases [Baitaluk et al. (2006) BMC Bioinformatics 7, 55]. Here we describe a server, BiologicalNetworks, which provides visualization, analysis services and an information management framework over PathSys. The server allows easy retrieval, construction and visualization of complex biological networks, including genome-scale integrated networks of protein–protein, protein–DNA and genetic interactions. Most importantly, BiologicalNetworks addresses the need for systematic presentation and analysis of high-throughput expression data by mapping and analysis of expression profiles of genes or proteins simultaneously on to regulatory, metabolic and cellular networks. BiologicalNetworks Server is available at

IntegromeDB: an integrated system and biological search engine

Abstract Background With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general-purpose search engines are not specialized for the search of biological data. Description Here, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene- and protein-centered knowledge. The engine is available at http://www.integromedb.org. Conclusions The IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and other gene- and protein-related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback

BiologicalNetworks 2.0 - an integrative view of genome biology data

Abstract Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other) and their relations (interactions, co-expression, co-citations, and other). The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org

PathSys: integrating molecular interaction graphs for systems biology

BACKGROUND: The goal of information integration in systems biology is to combine information from a number of databases and data sets, which are obtained from both high and low throughput experiments, under one data management scheme such that the cumulative information provides greater biological insight than is possible with individual information sources considered separately. RESULTS: Here we present PathSys, a graph-based system for creating a combined database of networks of interaction for generating integrated view of biological mechanisms. We used PathSys to integrate over 14 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and Gene Ontology. A number of exploratory questions were formulated as a combination of relational and graph-based queries to the integrated database. Thus, PathSys is a general-purpose, scalable, graph-data warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping. CONCLUSION: Results from several test studies demonstrate the effectiveness of the approach in retrieving biologically interesting relations between genes and proteins, the networks connecting them, and of the utility of PathSys as a scalable graph-based warehouse for interaction-network integration and a hypothesis generator system. The PathSys's client software, named BiologicalNetworks, developed for navigation and analyses of molecular networks, is available as a Java Web Start application at

BiologicalNetworks - tools enabling the integration of multi-scale data for the host-pathogen studies

<p>Abstract</p> <p>Background</p> <p>Understanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage.</p> <p>Results</p> <p>To study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use.</p> <p>Conclusions</p> <p>The developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at <url>http://www.biologicalnetworks.org</url>) and web interface (at <url>http://flu.sdsc.edu</url>).</p

IntegromeDB: an integrated system and biological search engine

Abstract Background With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general-purpose search engines are not specialized for the search of biological data. Description Here, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene- and protein-centered knowledge. The engine is available at http://www.integromedb.org. Conclusions The IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and other gene- and protein-related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback.</p

Integrative view of the OCT4 regulatory network (Use Case #1, Study 2).

<p>(<b>A</b>) Gene regulatory modular network of OCT4 transcription factor. Grey boxes represent the gene regulatory and co-expressed modules; rectangles represent the genes; red rectangles, the genes with known binding sites; a yellow triangle, the transcription factor; blue edges, TF-target gene relationships; red lines, co-expressed TF-gene pairs. The top module (shown in <b>C</b>), called ‘Module 1′, is highlighted. (<b>B</b>) GenomeBrowser window showing the sequences of the genes and TF binding sites. The OCT4 binding site for the selected in the network (<b>A</b>) <i>Pou2f1</i> gene is shown. (<b>C</b>) Module Table showing the gene modules, TFs, and functional annotation for each module with Fisher enrichment score (p-value) of GO terms. The top ‘Module 1′ is highlighted. (<b>D</b>) Table of TFs and target genes found in public databases. Gene <i>Pou2f1</i> (selected in <b>A</b>) is highlighted in magenta. (<b>E</b>) Multi-Experiment Viewer represents the matrix of genes (in columns) co-expressed with the query gene(s) in microarray experiments (in rows). (<b>F</b>) Microarray Gene Expression window showing the hit map and hierarchical tree of clustering data from selected experiments. Pointing out the mouse on the tree vertex shows the significant GO terms for the cluster; ‘Module 1′ is highlighted.</p

An Integrative Approach to Inferring Gene Regulatory Module Networks

<div><h3>Background</h3><p>Gene regulatory networks (GRNs) provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of gene regulatory modules and GRNs require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming.</p> <h3>Results</h3><p>In this work, the BiologicalNetworks application–the data integration and network based research environment–was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the gene regulation analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided.</p> <h3>Conclusion</h3><p>The generated modular networks, regulatory modules and binding sites can be visualized and further analyzed within this same application. The developed tools were applied to the mouse model of asthma and the OCT4 regulatory network in embryonic stem cells. Developed methods and data are available through the Java application from BiologicalNetworks program at <a href="http://www.biologicalnetworks.org">http://www.biologicalnetworks.org</a>.</p> </div

Screen-shot of the Multi-Experiment viewer (Use Case #1, Study 2).

<p>(<b>A</b>) The matrix represent the genes (in columns) co-expressed with the query gene(s) in microarray experiments (in rows). The brightness of blue of the matrix element corresponds to the co-expression value of the gene in an experiment (Eq. 4). The genes and experiments are sorted by average Z-values of genes (Eqs. 1–3). The vertical and horizontal levers allow selecting the highest ranked genes and experiments for building regulatory modules (the selection is shown in a black square). Hovering over the genes and experiments brings up their short description. (<b>B–C</b>) Clicking on the experiment ID brings up the experiment properties and visualization of the expression data. (<b>D</b>) A word cloud that characterizes the found set of experiments described by keywords (ontology terms representing cell types, tissues, diseases, biological processes, etc.). Clicking on the term in the cloud highlights respective experiments. The ‘Recalculate’ button allows the user to recalculate the matrix choosing only the experiments containing selected terms.</p