DRP007269
Multiplex genotyping method to validate the multiallelic genome editing outcomes using machine learning-assisted long-read sequencing
Genome editing can permit the introduction of designed mutations into the target genomic site. Recent work has uncovered that it also induces various unintended events such as structural variations, small indels and substitutions at, and in some cases, away from the target site. Overlooked rearrangements may result in confounding phenotypes in biomedical research samples and represent a concern for clinical or agricultural application. However, no genotyping method has allowed a comprehensive analysis of diverse mutations for phasing and mosaic variant detection from long-read sequencing data. We developed a genotyping method with an on-target site analysis software named Determine Allele mutations and Judge Intended genotype by Nanopore sequencer (DAJIN) that can automatically identify and classify both intended and unintended diverse mutations, including point mutations, deletions, inversions, and cis double knock-in at single-nucleotide resolution. Our approach with DAJIN can handle approximately 100 samples under different editing conditions in a single run. With its high versatility, scalability, and convenience, DAJIN-assisted multiplex genotyping may become a new standard for validating genome editing outcomes.