Gene Mutation Analysis for Functional Annotations Using Graph Heuristics

Research studies on pathogenic mutations are important and beneficial for understanding disease development, prognosis and gene-disease associations. Moreover, pathogenic mutations have harmful consequences which may lead to certain diseases or medical conditions. This paper presents a method for inducing the most significant and accurate functions for a given set of mutations having one common aspect, e.g. specific disease. The proposed method is based on the directed acyclic graph of the gene ontology to identify the most significant least common subsumers to be used for functionally annotating the mutations under investigation. We applied the method on a large sets of mutations. The reported results in this paper are encouraging and suggest that a mutation can have, and can be annotated with a function, e.g. biological process, from the gene ontology just like the genes, which contributes into a more complete understanding of mutation pathogenicity, mutation-gene-disease relationships, and disease mechanisms.