Dataset Information

Integration of biological networks and gene expression data using Cytoscape.

ABSTRACT: Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

SUBMITTER: Cline MS

PROVIDER: S-EPMC3685583 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Integration of biological networks and gene expression data using Cytoscape.

Cline Melissa S MS Smoot Michael M Cerami Ethan E Kuchinsky Allan A Landys Nerius N Workman Chris C Christmas Rowan R Avila-Campilo Iliana I Creech Michael M Gross Benjamin B Hanspers Kristina K Isserlin Ruth R Kelley Ryan R Killcoyne Sarah S Lotia Samad S Maere Steven S Morris John J Ono Keiichiro K Pavlovic Vuk V Pico Alexander R AR Vailaya Aditya A Wang Peng-Liang PL Adler Annette A Conklin Bruce R BR Hood Leroy L Kuiper Martin M Sander Chris C Schmulevich Ilya I Schwikowski Benno B Warner Guy J GJ Ideker Trey T Bader Gary D GD

Nature protocols 20070101 10

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with ge ...[more]

PMID: 17947979

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Project description:BackgroundA current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear.MethodsWe propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO.Results and conclusionsThis work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Dataset Information

Integration of biological networks and gene expression data using Cytoscape.

Publications

Integration of biological networks and gene expression data using Cytoscape.

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