Inferring the relationships among genes from weighted GO graph

Biologists may need to know the set of genes that are semantically related to a given set of genes. For instance, a biologist may need to know the set of genes related to another set of genes known to be involved in a specific disease. Some works use the concept of gene clustering in order to identify semantically related genes. Others propose tools that return the set of genes that are semantically related to a given set of genes. Most of these gene similarity measures determine the semantic similarities among the genes based solely on the proximity to each other of the GO terms annotating the genes, while overlook the structural dependencies among these GO terms, which may lead to low recall and precision of results. We propose in this paper a search engine called IRG, which overcomes the problems of current gene similarity measures outlined above. The search engine constructs a minimum spanning tree of GO graph based on their weights. Let S′ be the set of genes that are semantically related to set S. In the framework of IRG, the set S′ is annotated to the GO term located at the convergence of the subtree of the minimum spanning tree that passes through the GO terms annotating the set S. We evaluated IRG experimentally and compared it with a gene prediction tool called DynGO and with two other systems we proposed previously. Results showed marked improvement.