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    Genowiz

    Category  Genomics>Gene Expression Analysis/Profiling/Tools

    Abstract  Genowiz is an advanced gene expression analysis program
    that has been designed to store, process and visualize gene
    expression data efficiently. It includes a suite of advanced analysis
    methods and allows researchers to select analysis methods
    appropriate for their dataset. Genowiz allows researchers to organize
    experimental information (MIAME), import data files, work with multiple
    experiments at the same time, import gene annotation files, pre-
    process and normalize data, perform cluster analysis, classify and
    view gene information, perform functional classification and track down
    intricate correlations in data by performing pathway analysis. All
    analysis done is tracked, saved into a database and can be retrieved
    at any point of time.

    Products features/capabilities include:

    Data and Gene List Import - Genowiz supports a wide range of data
    formats pertaining to cDNA and Affymetrix data. Users can directly
    import cell intensity (.CEL) and summarized expression values (.CDF)
    files into Genowiz. Users also have the option to upload data in
    customized formats. The customized uploader allows users to add
    and save new data formats. Then, the One-Click Uploader can identify
    these formats. Gene List files for annotating genes can also be
    imported.

    MIAME - Minimum Information About a Microarray Experiment (MIAME)
    facilitates the adoption of standards for microarray experiment
    annotation and data representation. Genowiz focuses on establishing
    standard microarray experimental data repositories and information
    sharing within the scientific community. Researchers can also
    exchange MIAME data by using the MicroArray Gene Expression
    Markup Language (MAGE ML) document exchange format.

    Data Transformation, Normalization and Filtration - In any type of
    expression analysis, pre-processing of data to reduce undesirable
    variation among datasets and to bring data to a common platform is a
    vital step. Genowiz provides users with a wide range of data
    transformation, normalization and filtration tools. These include:

    1) Data transformation options such as imputation of missing values,
    log transformations, mean/median, Z-transformation, subtract control,
    divide by control, scaling etc.

    2) Normalization techniques such as normalization for dye swap
    replicates, cDNA raw data normalization options (cDNA Loess and
    Print tip Loess) and quantile normalization. Separate normalization
    techniques are provided for cDNA and Affymetrix arrays. Normalization
    can be done using all genes or control genes.

    3) Filter data based on replicate genes, fold change, mean, standard
    deviation, calls and missing values. Replicate samples are handled
    using various parametric/non-parametric tests. Multiple testing
    correction(s) can be applied to reduce false positives.

    Data Analysis and Visualization - Genowiz comes equipped with
    several data analysis tools. Complete with excellent graphics, it is an
    excellent tool for the interpretation of biologically meaningful results.
    Some of these tools include partition clustering, hierarchical
    clustering, SOM, PCA, gene shaving and discriminant PCA and SVM.
    An option for merging clusters of interest has also been provided. Data
    analysis tools are as follows:

    1) Partition Clustering (K-means, Forgy's) - This tool classifies genes
    or samples in user-defined groups using distance parameters. The
    obtained clusters can be re-clustered. The re-clustering utility helps
    scientists pick a set of genes of interest. A 2D PCA view shows the
    distribution of genes in various clusters.

    2) Hierarchical Clustering - One of the most important tools for
    studying relations between genes, this tool creates a dendrogram
    based on the relative distance between genes. Different optional
    parameters help the user to correctly determine the relationship
    between two genes. Models of analysis include single linkage,
    complete linkage and average linkage clustering. Genes, samples, or
    both together can be clustered.

    3) Self Organizing Maps (SOMs) - A two-way classification of genes
    into clusters based on novel artificial neural networks is an integral
    feature of the data clustering tools in Genowiz. This function gives you
    a deeper insight into clusters, based on the fact that neighboring
    clusters are very similar to each other.

    4) Principal Component Analysis (PCA) - This tool involves a
    mathematical procedure that transforms a number of (possible)
    correlated variables into a (smaller) number of uncorrelated variables
    called principal components. This function provides insight into
    'existent variability' in the data.

    5) Gene Shaving - This method identifies subsets of genes with
    coherent expression patterns and a large variation across conditions.
    Gene shaving differs from hierarchical clustering and other methods of
    gene expression analysis in that those genes may belong to more
    than one cluster.

    6) Classification - Classification algorithms are used to classify
    samples, based on information from similar samples with known
    classes that are available in the training data. In Genowiz, Support
    Vector Machines (SVMs) and Discriminant PCA are used to predict
    classes for unclassified samples.

    Biological Analysis - Genowiz annotates genes and classifies them
    into functional categories [Gene Ontology (GO)]. An option to import
    annotation files is also provided. The integrated pathways module aids
    researchers to understand metabolic pathways in relation to
    expression data. Pathway maps can be edited and/or created by the
    end-user. Search(s) can be performed on the gene ontology and
    pathway tree to look for ontologies or pathways of interest.

    Utilities - The following utility options/features are available:

    1) Gene List Comparison - Subtle relations among datasets can be
    probed using this feature.

    2) Pattern Simulation - An expression pattern can be defined and
    Genowiz has the ability to list all genes with a similar expression
    pattern. This gene list can then be saved and exported.

    3) Gene Tracking - Important genes or genes of interest can be tagged
    and tracked throughout the analysis process.

    View and Update NetAffx annotations - Annotations for uploaded data
    can be viewed by connecting to the NetAffx database. Connecting to the
    NetAffx database and selecting a corresponding chip will retrieve
    annotations from that chip. The flexibility to update annotation
    information for existing chips and add annotation information for new
    chips is also present, thus enabling researchers to view updated
    annotations for chips.

    Note: See G6G Product Number 20073 for additional product info from
    this manufacturer.

    System Requirements  

    Windows:
    Supported Operating Systems: Windows 98, Windows ME, Windows
    NT, Windows 2000, Windows XP.
    Memory(RAM): Minimum - 512 MB. Recommended - 1 GB.
    Display: 1024 x 768
    Free Disk Space: Minimum - 300 MB. Recommended - 500 MB.

    Mac:
    Supported Operating Systems: Mac OS X v10.2.8 or later.
    Memory(RAM): Minimum - 512 MB. Recommended - 1 GB.
    Display: 1024 x 768
    Free Disk Space: Minimum - 300 MB. Recommended - 500 MB.

    Unix:
    Supported Operating Systems: Linux or Solaris.
    Memory(RAM): Minimum - 512 MB. Recommended - 1 GB.
    Display: 1024 x 768
    Free Disk Space: Minimum - 300 MB. Recommended - 500 MB.

    Manufacturer   Home office; see web site for international locations.

    Ocimum Biosolutions Ltd.
    6th Floor, Reliance Classic
    Road No. 1, Banjara Hills
    Hyderabad - 500 034. A.P., India
    Phone: +91-40-6662-7200
    Fax: +91-40-6662-7205
    Email: hyd@ocimumbio.com
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