Optimsed Exomiser parameters being used to generate candidate variants in Mosaic

Accurate variant prioritization remains a major bottleneck in rare disease diagnostics. In the recently published paper “An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser,”, the team from the University of Utah, Stanford University, Harvard Medical School, and Frameshift, provide a practical, evidence-based framework for improving the diagnostic yield and reducing the time to diagnosis using Exomiser.

We evaluated how Exomiser and Genomiser perform across real-world clinical scenarios, using diagnosed participants from the Undiagnosed Diseases Network (UDN), resulting in clear recommendations on parameter settings, filtering strategies, and interpretation workflows. A key finding is that a well configured Exomiser analyses can reliably elevate clinically relevant variants—often surfacing diagnoses that might otherwise be missed amid thousands of candidates. Importantly, we demonstrated the need for accurate phenotyping, relying on clinical expertise.

Mosaic makes Exomiser-prioritized variants immediately accessible within our intuitive, collaborative interface, ensuring that clinical teams can explore, interrogate, and re-evaluate results together. By combining optimized Exomiser recommendations with structured variant organization and seamless reanalysis, Mosaic helps teams move more efficiently from candidate variants to confident clinical decisions. Robust variant prioritization is not just about algorithms, but about empowering clinical teams with the right tools at the right time. Mosaic brings these best-practice recommendations into everyday diagnostic workflows, helping translate cutting-edge methodology into real impact for patients with rare disease.

Find additional information about Exomiser here, or visit https://frameshift.io/mosaic or reach out to info@frameshift.io if we can help you in your genomic research efforts!