This technology is a computational tool that leverages existing biological knowledge of phenotype-gene relationships to identify genes involved in human diseases.
Within the vast library of human genome/exome sequencing data, only a handful of gene variants are responsible for the phenotypic differences observed in congenital diseases. Using prior biological knowledge and phenotype information can help identify the causal genes that contribute to disease development. While several prioritization tools use known genotype-phenotype relationship information, the input for these tools is limited to training gene lists or specific disease or ontology identifiers. These requirements limit usage and accessibility to average biologists. In order to better leverage and integrate known phenotype information in the study and diagnosis of human diseases, there is a need to develop a user-friendly tool that uses existing disease nomenclature and can broadly interpret user input.
This technology is a computational tool called Phenolyzer that prioritizes human disease genes based on disease or phenotype information provided by users as free text. The technology includes multiple components: (1) a tool to map user-supplied phenotypes to related human diseases, (2) a database of information on known disease genes, (3) an algorithm that predicts unknown disease genes, and (4) a network visualization tool to investigate gene-gene and gene-disease relationships. The user-friendly web server takes user input and generates results within minutes. The results include a summary tab with word cloud and links to output files, a gene-disease-term interaction network, a bar plot of the top 500 ranked genes with normalized scores, and detailed record-tracking information with raw scores and links to external databases. When combined with the ANNOVAR computational tool, users will be able to prioritize disease variants from genome/exome sequencing data. As such, this technology has the potential to facilitate studies of human diseases by leveraging large amounts of pre-existing genetic information.
This technology has been demonstrated to provide superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text.
IR CU17141
Licensing Contact: Joan Martinez
