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Enhancing variant interpretation: Obtaining answers from a vast universe of data 

Pinpointing pathogenic mutations from large, complex datasets can be difficult, time consuming and somewhat overwhelming. So, how can you best streamline your genomic analysis, to make it quicker, easier and more efficient? In this webinar, we will demonstrate how variant interpretation software can improve the accuracy and speed of variant interpretation, using specific case studies, such as detecting compound heterozygous variants in Charcot-Marie-Tooth Disease, and Alu insertions in breast cancer.  

Rolph Pfundt, a Clinical Molecular Geneticist at Radboud University Medical Centre in the Netherlands, will share insights into his variant interpretation workflow. He will also highlight how Alamut™ Visual Plus enables the identification of complex variants with greater ease and precision.  

From this webinar, you will learn how to: 

  • Resolve splice-site variants using splicing scores and exonic splicing enhancer binding site predictors in a visual interface.  
  • Conduct familial variant analysis (FVA) for prioritization of germline variants, by excluding those that do not follow Mendelian inheritance patterns. 
  • Utilize trusted databases, predictors, publications and guidelines, such as the ACMG criteria, for effective genomics interpretation. 
  • Enhance interpretation through a software that offers comprehensive annotations and visualization of breakpoints and coverage across different file types in a full genome browser. 
  • Better understand the significance of a guideline-driven approach to variant nomenclature for consistent analysis and reporting.  

Speakers: 

  • Rolph Pfundt, Clinical Molecular Geneticist, Radboud University Medical Centre  
  • Swati Tomar, Commercial Application Product Manager, Rare and Inherited Diseases, SOPHiA GENETICS  

This webinar has been produced with the kind support of SOPHiA GENETICS. You can find out more about the technologies outlined in this webinar by visiting the SOPHiA GENETICS website.