Category Genomics>Gene Expression Analysis/Profiling/Tools and Cross-Omics>Pathway Analysis/Gene Regulatory Networks/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 that is done is tracked, saved into a database and can be retrieved at any point of time.

Genowiz 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.

The Automatic Uploader can then identify these formats. At the time of data import replicate genes are filtered, dye swap samples and thresholds for maximum and minimum values can be specified. PLIER, RMA and MAS 5.0 algorithms for Affymetrix probe level normalization and summarization are present.

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 are 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, and scaling.

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 like Two Way ANOVA, Mann Whitney U Test, Kruskal Wallis Test, and One Sample t-Test.

Multiple testing correction options such as, Bonferroni/FDR can be applied to reduce false positives.

Data Analysis and Visualization -- Genowiz™ is equipped with several data analysis tools. Complete with informative 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 and Visualization tools are as follows:

1) Partition Clustering (K-means) - 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 is provided that displays 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 (2) 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 (NN) 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.

7) Box Plot & Scatter Plot - Sample distribution can be visualized and compared within an experiment using Box plots and Scatter plots.

Fold change lines can be drawn on scatter plots. Up and down regulated genes can be color coded and selected genes can be saved as a node. Additional utility options have also been provided.

Biological Analysis -- Genowiz annotates genes and classifies them into functional categories [Gene Ontology (GO)]. An option to import annotation files and NetAffx™ chips information is also provided.

The integrated pathways module aids researchers in understanding pathways in relation to expression data. Pathway maps edited/created can also be associated with author details.

Coupled with biological information, gene ontology (GO), pathway classification; forms an excellent tool in understanding biological systems. The Annotations tab displays selected gene(s) annotations. Search can be performed on gene ontology and pathway tree(s) to find ontologies or pathways of interest.

1) Saving Gene Ontology and Pathway Analysis - Gene ontology and pathway analyses performed on different experiments are saved to the database. Functional clusters of interest can also be saved as gene lists and be compared using the List Comparison feature.

2) Add Organisms - Genowiz supports seven (7) organisms by default. Researchers can add organisms and genomes of interest and update corresponding annotations.

3) Automatically update Genowiz Annotations - Annotations can be updated for organism(s), either by connecting to respective websites or from a local disk (in case of internet connection, firewall, licensing or download problems).

Information regarding gene ontology, pathways, organisms and annotations from Entrez Gene database can be updated.

Utilities -- The following utility options/features are available:

1) Gene List Comparison - Subtle relations among datasets can be probed using this feature. Up to four (4) gene lists can be compared with a Venn diagram representation.

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.

Import/Export Experiments -- All analyses performed on an experiment can be exported to a specified location and the analyses performed on an experiment can also be imported into Genowiz. This facilitates sharing of experiments.

Work Flows -- Automated work flows for single, double and dye swap experiments effectively guide researchers in their analysis. User created work flows can be saved and applied to experiments.

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 Abstract Number 20073R for additional product info from this manufacturer.

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Manufacturer Web Site Genowiz

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G6G Abstract Number 20074R

G6G Manufacturer Number 102025