GOSim--an R-package for computation of information theoretic GO similarities between terms and gene products - PubMed (original) (raw)

GOSim--an R-package for computation of information theoretic GO similarities between terms and gene products

Holger Fröhlich et al. BMC Bioinformatics. 2007.

Abstract

Background: With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO).

Results: We present the freely available software package GOSim, which allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. GOSim extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. GOSim hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.

Conclusion: GOSim is implemented as a package for the statistical computing environment R and is distributed under GPL within the CRAN project.

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Figures

Figure 1

Figure 1

Example of a GO graph starting with leaves GO:0007166 and GO:0007267.

Figure 2

Figure 2

Idea of an optimal assignment: each GO term belonging to gene 2 is assigned to exactly one GO term belonging to gene 1 such that the overall GO term similarity is maximized.

Figure 3

Figure 3

Genes embedded into a feature space defined by the GO similarity to certain prototype genes. principal components analysis was used to reduce the dimensionality of the feature space and the first two principal components are displayed.

Figure 4

Figure 4

Clustering silhouette of the upregulated genes (cDNA chips).

Figure 5

Figure 5

Clustering silhouette of the downregulated genes (cDNA chips).

Figure 6

Figure 6

Clustering silhouette of the upregulated genes (Affymetrix chips).

Figure 7

Figure 7

Clustering silhouette of the downregulated genes (Affymetrix chips).

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