VANTED: A system for advanced data analysis and visualization in the context of biological networks

BACKGROUND: Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes. RESULTS: We present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature. CONCLUSION: VANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at

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

PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways

While generation of high-throughput expression data is becoming routine, the fast, easy, and systematic presentation and analysis of these data in a biological context is still an obstacle. To address this need, we have developed PathwayExplorer, which maps expression profiles of genes or proteins simultaneously onto major, currently available regulatory, metabolic and cellular pathways from KEGG, BioCarta and GenMAPP. PathwayExplorer is a platform-independent web server application with an optional standalone Java application using a SOAP (simple object access protocol) interface. Mapped pathways are ranked for the easy selection of the pathway of interest, displaying all available genes of this pathway with their expression profiles in a selectable and intuitive color code. Pathway maps produced can be downloaded as PNG, JPG or as high-resolution vector graphics SVG. The web service is freely available at ; the standalone client can be downloaded at

PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database

BACKGROUND: Equally important and challenging as genome annotation, is the subsequent classification of predicted genes into their respective pathways. The Kyoto Encyclopedia of Genes and Genomes (KEGG) represents a database consisting of known genes and their respective biochemical functionalities. Although accessible online, analyses of multiple genes are time consuming and are not suitable for analyzing data sets that are proprietary. RESULTS: Presented here is a new software solution that utilizes the KEGG online database for pathway mapping of partial and whole prokaryotic genomes. PathwayVoyager retrieves user-defined subsets of the KEGG database and stores the data as local, blast-formatted databases. Previously selected datasets can be re-used, reducing run-time significantly. Whole or partial genomes can be automatically analyzed using NCBI's BlastP algorithm and ORFs with similarities below the user-defined threshold will be marked on pathway maps. Multiple gene hits are sorted by similarity. Since no sequence information is transmitted over the Internet, PathwayVoyager is an ideal solution for pathway mapping and reconstruction of confidential DNA sequence data. CONCLUSION: PathwayVoyager represents an alternative approach to many already existing, more complex pathway reconstructions software solutions. This software does not require any dedicated hardware or software and is flexible and straightforward to use. It is ideally suited for environments where analyses on variable datasets are desired

Metabolic network visualization eliminating node redundance and preserving metabolic pathways

<p>Abstract</p> <p>Background</p> <p>The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult.</p> <p>Results</p> <p>We propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster.</p> <p>Conclusion</p> <p>The method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism.</p

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further

A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis

Motivation: Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks

A generic algorithm for layout of biological networks

BackgroundBiological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration.ResultsWe present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks.ConclusionThe presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.publishe