The research in our lab focuses on developing bioinformatics approaches for interpreting the functionality of genetic variants from genome sequencing data. In my talk, I will describe a computational tool called Phenolyzer, which analyzes user-supplied list of phenotype terms and assigns most likely candidate genes that are associated with the phenotypes, by integrating multiple sources of gene-pathway-disease-phenotype information. The combination of Phenolyzer and a variant annotation tool such as ANNOVAR allows researchers to quickly identify disease genes from sequence data and phenotype data on patients. I will also describe iCAGES (integrated CAncer GEnome Score), which leverages Phenolyzer and other information for prioritizing cancer driver genes/variants for a patient using genome sequencing data. Altogether, these tools will help better understand the functional consequences of genetic variants, and ultimately facilitate the implementation of precision medicine.