Transparent AI, Clinical Control: A Geneticist's Perspective on Variant Prioritization
Variant prioritization is one of the slowest, highest-stakes steps in exome and genome analysis, and it only gets harder as labs move toward whole-genome and long-read sequencing.
In our webinar, Joakim Klar (Uppsala University Hospital) and Alexandre Boulat (SeqOne) show how a leading clinical lab brought a complex, multi-technology workflow onto a single platform. Joakim explains why his team sought a new solution and how they now interpret SNVs, CNVs, structural variants, and long-read data in-house, illustrated with real cases from routine practice. Alexandre then takes attendees under the hood of DiagAI, an explainable model that shows exactly why each variant is ranked where it is, and presents results from whole-genome cases at Genomics England, where 98% of causal variants fell within the top 10 candidates.
In this webinar, you will discover:
- Unified workflows: How a leading lab brought short-read, long-read, and array data together on one platform.
- Faster review, same sensitivity: Practical ways to cut variant review time without missing what matters.
- Explainable AI you can evaluate: How to critically assess explainable AI for your own lab, and what transparent ranking looks like in practice.
- What's next: Emerging approaches to structural variant and long-read interpretation at scale.
Meet the Speakers
Joakim Klar, PhD
Clinical Laboratory Geneticist at Uppsala University Hospital and researcher in the Department of Immunology, Genetics, and Pathology at Uppsala University. He applies exome, genome, and RNA sequencing to the study and clinical diagnosis of rare genetic disorders, and has authored or co-authored more than 90 peer-reviewed publications in human genetics.
Alexandre Boulat
Data Science Engineer at SeqOne, where he integrates AI into software solutions for precision medicine. A passionate advocate for education, he presents the science behind tools like DiagAI and holds a dual master's degree in data science and information systems engineering.
Watch the full replay to see how explainable AI is helping clinical labs move faster on even the most complex cases.
