Genomic sequencing is rapidly becoming a standard tool in diagnosing complex diagnostic cases, especially incritically ill newborns, and patients suffering from rare diseases. Current programs focused on addressingthese cases utilise a team-based approach to identify, interpret and evaluate a patient's genetic variants.These diverse teams include bioinformaticians, medical geneticists, genetic counselors, and physicians amongothers, and encompass a wide range of expertise, skills, and literacy with computational methods. Thecomplexity of evaluating a genetic variant's role in causing a patient's specific set of phenotypes requires theexpertise and unique knowledge of all members of the team, and efficient methods of communication tosupport rapid, asynchronous analysis of all potentially interesting observations. Currently, team membersperform analyses based on their own expertise, and rely on email communication, and regular in-personmeetings to integrate the knowledge of the team. This process is very time-intensive, and inefficient, especiallywhen previously undiagnosed cases are reviewed, and critical case information is hidden in myriad emailthreads, presentations etc. This proposal brings together a highly popular tool for rapid visual analysis ofgenetic variants with a commercial data management solution to provide a tool set to support team-basedgenomic medicine. Our gene.iobio tool performs real-time analysis of a patient's genome in an easy-to-useweb-based application, ensuring all team members can efficiently contribute their expertise in the diagnosticprocess. This tool will be expanded with critica features, and brought up to a commercial standard ofdeployment to ensure it can deliver on its potential in a reliable and repeatable manner. Our Mosaic toolprovides HIPAA-compliant access to distributed data, role-based authentication, as well as comprehensivevisualization, and communication features. This tool will also be expanded with functionality to supportintegration with gene.iobio, as well as collaboration features designed to support this community. By improvingthe features of both of these tools; providing analysis versioning; developing a deep API based integrationbetween them; and ensuring the combined package is fast, reliable and robust, we will deliver a softwareproduct that is currently absent in the market; one that will improve collaboration on cases, save significantamounts of time for all team members, and ultimately use these efficiency gains to ensure that genomicmedicine can be scaled up to serve more patients moving forwards. We will deploy this package in our ownrapid NICU sequencing program, and UDN clinical site to evaluate and refine the product.
Public Health Relevance Statement: Narrative
We are developing a software package to support team-based, genomic analysis of challenging diagnostic
cases. This development focuses on significantly improving and integrating existing software tools to offer a
commercially viable product to fill a growing need in the market. We will test the utility and effectiveness of our
software by deploying in two of our own diagnostic analysis programs at the University of Utah.
Project Terms:
