ABSTRACT
Clonal mosaicism (a detectable post-zygotic mutational event in cellular subpopulations) is common in cancer patients. Detected segments of clonal mosaicism are usually bundled into large-locus regions for statistical analysis. However, low-frequency genes are overlooked and are not sufficient to elucidate qualitative differences between cancer patients and non-patients. Therefore, it is of interest to develop and describe a tool named Sub-GOFA for Sub-Gene Ontology function analysis in clonal mosaicism using semantic similarity. Sub-GOFA measures the semantic (logical) similarity among patients using the sub-GO network structures of various sizes segmented from the gene ontology (GO) for clustering analysis. The sub-GO's root-terms with significant differences are extracted as disease-associated genetic functions. Sub-GOFA selected a high ratio of cancer-associated genes under validation with acceptable threshold.
