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    VANTED

    Category  Cross-Omics>Pathway Analysis/Tools

    Abstract  The VANTED (Visualization and Analysis of NeTworks
    containing Experimental Data) software system for transcriptomics,
    proteomics and metabolomics analysis is a platform-independent
    system which enables researchers to evaluate extensive biochemical
    data in an easy way.

    VANTED supports the integrated analysis of data for different growth
    conditions or transgenic lines from optionally different time points.

    It uses the KEGG Pathway database, which includes a comprehensive
    set of 'pathway maps' representing knowledge about metabolism, and
    the Gene Ontology (GO), which contains a hierarchy of controlled
    vocabulary to describe gene and gene products in organisms.

    Such networks and hierarchies, either imported, modified or newly
    created by the user, serve as the basis for different methods for
    mapping, visualization and analysis of data.

    Not all imaginable use-cases may be supported by a software system,
    and therefore the possibility of enhancing an application with newly
    developed analysis, visualization and data exchange methods is of
    great importance for many researchers.

    A Java and Ruby script-interface and interpreter allows the user to
    dynamically extend the VANTED system for such purposes.

    VANTED Network Generation and Data Mapping --

    For the analysis of experimental data it is sensible to apply an
    integrated view of the measured data and its related background
    information, such as ‘metabolic pathways’ or regulative processes.

    This approach corresponds to the general idea of systems biology,
    where a biological system is analyzed Not only by studying a single
    phenomenon, but by considering a broader view which includes all
    elements of a biological system.

    To achieve this, three (3) aspects were important for the design of the
    VANTED software system:

    1) VANTED supports 'dynamic networks'.

    Networks can be imported from databases (e.g. KEGG) or may be
    loaded from files in different formats - Graph Modeling Language (GML),
    Systems Biology Markup Language (SBML), Systems Biology Graphical
    Notation (SBGN), and Pajek-.NET [Pajek (Slovene word for Spider) is a
    program, for Windows, for the analysis and visualization of large
    networks].

    It is also possible to create networks by hand with an integrated
    graphical editor. A big advantage of dynamic networks is the possibility
    of customizing them easily for different requirements. For instance,
    networks can be easily extended when more substances are measured.

    2) The integration of measured data and relevant network elements is
    supported. An automatic mapping of data onto relevant network
    elements occurs if the measured data and the network nodes have
    common identifiers.

    Also during this mapping procedure synonyms are used as long as
    they are included in one of the supported databases (e.g. the SIB
    Enzyme nomenclature database from the Swiss Institute of
    Bioinformatics).

    If an automated integration is Not possible, a new ‘graph node’ is
    generated, which is then used for data mapping. Additionally, data may
    be assigned manually to user-given network elements.

    (3) VANTED supports the display of 'multiple values' on a single network
    element.

    While some approaches often support only the coloring of network
    elements based on single values (e.g. directly measured data or a
    computed factor, such as comparison of two different datasets), the
    inclusion of diagrams in the network representation allows the
    visualization of more complicated data.

    An additional advantage which arises from the use of line charts or bar
    charts is the easy interpretation of such a representation.

    VANTED Statistical Tests --

    The measured data of a sample varies around a mean value because
    of measuring inaccuracies and biological variability. When a wild-type of
    a plant is compared to different other lines, or the plant is exposed to
    environmental stress, it is of interest whether the sample means differ
    significantly or Not.

    For normally distributed data two variations of the t-test can be applied.
    Depending on the assumption of equality of variances, Student’s
    unpaired t-test or the Welch-Satterthwaite t-test can be carried out.

    Whether a sample is normally distributed can be checked within
    VANTED with the built-in Davidquick test. The measurements which do
    Not fulfill this criterion are marked and can then be examined separately.

    As an alternative to the t-test, the U-test is provided, which may also be
    used for Not normally distributed data. Another phenomenon is outliers
    in the dataset which can be identified in VANTED based on the Grubbs
    test.

    VANTED Correlation Coefficients --

    Relations between different measured substances can be recognized
    with XY diagrams. Here VANTED allows the selection of a number of
    'substance nodes' from the network view. These substances are pair-
    wise related to each other and displayed in an array of XY diagrams.

    The correlation factor between each two substances is computed and
    visualized using different colors of the diagram borders.

    In a similar way, the correlation of a user-selected substance to all other
    substances can be determined. A positive or negative correlation
    between the selected substance and another substance becomes
    immediately visible by different node background colors.

    Furthermore it is possible to determine statistically significant
    correlations between all measured substances which can be visualized
    by new graph edges connecting significantly correlated graph nodes.

    VANTED Automated Data Clustering --

    To recognize typical patterns in the temporal courses of the substance
    concentrations, VANTED includes a neural network (NN) algorithm, the
    Self-Organizing Map (SOM).

    In the first phase of this algorithm a given number of typical profiles of
    substance concentrations over time are determined.

    For instance, the substance concentration may increase in one group of
    substances during the time and decrease in another.

    In the second step every measured substance is assigned to the best
    suitable pattern. Each substance afterwards belongs to a specific group.

    In the 'graphical network' view the grouped data sets can then be
    separated and individually analyzed.

    System Requirements  

    Contact manufacturer

    Manufacturer   

    VANTED is mainly developed and designed by Christian Klukas,
    working in the Network Analysis Group of Dr. Falk Schreiber at the IPK-
    Gatersleben.

    Christian Klukas
    Network Analysis Group
    Corrensstr. 3
    06466 Gatersleben
    Germany
    Tel: ++49 39482 5 763
    E-Mail: klukas@ipk-gatersleben.de
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