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Module Networks
Cancer Module Map
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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:


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. We applied this tool to construct a module map of cancer.

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 coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied this method to construct a regulatory network underlying the response of yeast to stress.

Find which functional groups are enriched in 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.

Browse and analyze results in chromosomal coordinates with the 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 Genmica's genome browser, 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.


   Genomica is participating in the GenomeSpace initiative.