KAGE 2: Fast and accurate genotyping of structural variation using pangenomes

Abstract

AbstractStructural variation is known to play an important and often overlooked role in regulating disease and traits, but accurately detecting structural variants from sequencing data has traditionally been difficult. However, recent improvements in high-quality genome assembly along with methodological advancements in pangenome creation have opened up the landscape for methods that use such pangenomes for structural variant calling and genotyping. We here present KAGE2, which accurately and efficiently genotypes structural variation by exploiting the availability of pangenomes that represent known variation in a population. Through comprehensive benchmarking, we highlight limitations of existing methodology and show that KAGE2 is more accurate and considerably faster than existing methods.