![]() Each tool has a distinct set of features, which are highlighted in Table 1. For example, the HPRD repository more than doubled in size between 20 22.Ī number of software tools are available for network visualization and analysis, including Osprey 23, VisANT 24, CellDesigner 25 GenMAPP 26, PIANA 27, ProViz 28 BioLayout 29, PATIKA 30 and Cytoscape 31. These public data repositories are growing rapidly as the underlying measurement technology improves. Literature association networks are also useful as a general literature search tool, since each link is tied to the supporting publication. This link may indicate a biochemical association, such as catalysis, or a genetic, colocalization or coexpression relationship. In these networks, two genes are linked if they are frequently mentioned in the same sentence 21. When information is not available in databases but is in the literature, text-mining techniques can extract functional relationships between recognized genes that, while not always accurate, are useful for analysis in aggregate 20. Protein–DNA interactions mapped at the genome scale using ChIP-Chip and ChIPSeq technology 19 provide potential links between transcription factors and their regulated genes. Protein–protein interactions mapped either by focused studies or by high-throughput techniques are increasingly available in public repositories such as IntAct 15, HPRD 16 and MINT 17 (as reviewed in ref. The Gene Expression Omnibus 13 and ArrayExpress 14 are both large public repositories of gene expression profiles. Many sources of expression profiles and cellular networks exist. For instance, it is estimated that up to 50% of unfiltered yeast two-hybrid data are spurious 10, although this is improving as experimental protocols and automated reliability measures that combine multiple data sets of a given type evolve 11, 12. This is important because expression profiles can be noisy and difficult to reproduce when expression levels are low 9, while protein interaction assays are known to contain false positives and negatives. For instance we can identify a transcription factor known to regulate a set of affected genes.Īn important benefit of integrating expression and network data is biologically relevant signals supported by both data types are more likely to be correct than those supported from either data source alone. By combining expression profiles with cellular network information, including protein–protein and protein–DNA interactions, we can begin to explain the control mechanisms underlying the observed changes in activity of a biological process. Commonly used expression analysis methods identify active biological processes from expression profiles by finding enriched gene annotation terms in the lists of differentially expressed genes 5– 8. These measurements have significant potential to map cellular processes and their dynamics, given the appropriate computer software to filter and interpret the resulting large amount of data. Functional genomic and proteomic techniques enable routine measurement of expression profiles and functional interactions from the cells and tissues of many different organisms 1– 4.
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