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 efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-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, different regions of the ssODN have variable influence, which reflects the underlying mec

SUBMITTER: O'Brien AR 

PROVIDER: S-EPMC6391469 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature