Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract Genomica is an analysis and visualization tool for genomic data, which can integrate gene expression data, DNA sequence data, and gene and experiment annotation information.

Using Genomica, you can do the following:

1) Create a Module Map: Characterize Expression Data using Gene Sets --

A module map describes 'expression profiles' in an expression data of interest in terms of the 'behavior of modules', sets of genes that act in concert to carry out a specific function.

By creating a module map, you can extract modules and characterize 'gene-expression profiles' as a combination of activated and deactivated modules or gene sets.

Since gene sets have biological meaning, such a characterization provides an informative view of the expression data. The manufacturer applied this tool to construct a 'module map' showing conditional activity of expression modules in cancer.

As a general overview, the procedure for creating a module map starts with an expression data and gene experiment sets of interest.

In the first step, for each gene set, the procedure identifies all the arrays in which the gene set is significantly up- (down-) regulated using a statistical test based on the hypergeometric distribution.

In the second step, the set of arrays in which each gene set is significantly up- (down-) regulated is tested for enrichment relative to each of the available experiment sets, resulting in a map of gene sets versus the experiment sets in which they are significantly up- (down-) regulated.

A map can also be created without experiment sets, in which case the resulting map will consist of gene sets versus the individual experiments in which they are significantly up- (down-) regulated.

There is also the option of automatically merging gene sets with similar expression patterns, and refining the gene sets into modules that only include the genes that are significantly consistent with the arrays in which a gene set significantly changes.

Finally, once a module map is constructed, you can also return to the gene level and examine detailed views of the original data from which the significant associations were derived.

2) Create a Module Network: Identify 'Regulatory Networks' from Expression Data --

A module network is a 'probabilistic model', based on probabilistic graphical models and 'Bayesian networks', for identifying 'regulatory modules' from gene expression data.

The procedure identifies modules of co-regulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'.

The manufacturer applied this method to construct a 'regulatory network' underlying the response of yeast to stress.

3) Find which functional groups are enriched in a gene set --

In many methods for analyzing genomic data in general and 'gene expression' in particular, the end result is some collection of 'gene sets' (e.g., clusters of co-expressed genes or 'functional modules').

At that point, we are often interested in knowing whether genes assigned to the same gene set are functionally related.

Genomica allows you to automatically answer this question, by loading pre-existing gene sets compiled from other sources and checking which of them are enriched in the gene set collection.

For instance, in a clustering application, you can instantaneously identify which functional groups are enriched in each cluster.

You can also use Genomica to identify the 'biological processes' represented by the genes in each cluster, by creating a gene set collection from the clusters, and checking for enrichment of this collection against a collection of gene sets that represent biological processes, such as the Gene Ontology (GO) database of functional annotations.

The procedure to do this is simple - given two (2) collections of gene sets, every pair of gene sets are compared using a statistical test based on the hypergeometric distribution and significant overlaps are reported.

4) Browse and analyze results in chromosomal coordinates with the Genomica Genome Browser --

Many types of data are best represented in genomic coordinates.

Examples include ChIP-chip and CGH data from tiling microarrays, conservation data, and nucleosome positions.

To visualize such data you can use Genomica's Genome Browser (see G6G Abstract Number 20341), which provides expanded browsing capabilities compared to common web genome browsers.

More importantly, Genomica provides a suite of tools that perform statistical tests between data in chromosomal coordinates.

For instance, you can quickly find the types of chromosomal regions that are significantly proximal to regions bound by a factor in a ChIP-chip experiment.

Note: Genomica has evolved out of the GeneXPress software, developed at Stanford University, USA.

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Manufacturer Web Site Genomica

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G6G Abstract Number 20340

G6G Manufacturer Number 104001