San Diego, USA – October 17th, 2018 – SOPHiA GENETICS, leader in Data-Driven Medicine, has announced today from the annual meeting of the American Society of Human Genetics (ASHG), the release of Alamut Genova, the advanced variant exploration software for genomic data visualization and interpretation.
Alamut Genova is the latest evolution of Alamut Visual, already trusted by over 500 healthcare institutions worldwide. The powerful new decision-support technology, Alamut Genova is now a full genome browser, further enabling clinical researchers to easily assess the pathogenic status of human alterations. The software graphically displays complex genomic information, integrated from various curated sources and prediction algorithms in one user-friendly environment.
This new version has been redesigned to empower geneticists to solve complex genomic interpretation cases more intuitively. Alamut Genova offers a series of novel and efficient features, including ACMG/AMP classification, 3D protein visualization, Sanger electropherogram visualization and new splicing predictions that complete the existing solution.
Combining the analytical power of SOPHiA AI platform with the advanced visualization software Alamut Genova, geneticists can now benefit from a comprehensive solution to precisely identify and fully explore variants associated with hereditary disorders and cancer. SOPHiA accurately detects and annotates all types of alterations and redirects complex variants to Alamut Genova in order to assess them in the exact genomic context. This is particularly impactful for large gene panels, full exomes and genomes, where a large number of newly detected genomic variants and variants of unknown significance (VUS) are identified.
André Blavier, Scientific Director at SOPHiA GENETICS, said: “The combined use of SOPHiA and Alamut Genova provides clinical researchers with a unique and complete solution, supporting them all the way from precise genomic variant detection to proper interpretation. This new release marks a step forward in increasing experts’ adoption of Data-Driven Medicine.”
