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). GOSim Package: 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. Since version 1.1 GOSim additionally offers the possibility of a GO enrichment analysis using the topGO package. Moreover, since version 18.104.22.168 GOSim offers some lately developed diffusion kernel techniques to compute similarities between GO terms (see references in the vignette). Hence, GOSim acts now as an umbrella for different analysis methods employing the GO structure. Implementation: GOSim is implemented as a package for the statistical computing environment R and is distributed under GPL within the CRAN project. It includes documentation and examples on how to use the package.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Telesca, Donatello; Müller, Peter; Parmigiani, Giovanni; Freedman, Ralph S.: Modeling dependent gene expression (2012)
- Porzelius, Christine; Johannes, Marc; Binder, Harald; Beißbarth, Tim: Supporting information leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients (2011)