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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants
A rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence anddoi:10.1101/739474 fatcat:gbs3m6hw6rfwfp5zg5o2mom2yy