Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning

Abstract

Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated “base editing” being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 3-fold lower efciency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efciency-governing factors on a novel mouse dataset using machine learning. We found the sequence composition of the single-stranded oligodeoxynucleotide (ssODN), i.e. the repair template, to be a governing factor. Furthermore, diferent regions of the ssODN have variable infuence, which refects the underlying mechanism of the repair process. Our model improves HDR efciency by 83% compared to traditionally chosen targets. Using our fndings, we developed CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or – for-the-frst-time–HDR-mediated nucleotide changes.