Unlocking HDR-mediated Nucleotide Editing by identifying high-efficiency target sites using machine learning

BioRxiv : the Preprint Server for Biology
Aidan O'BrienDenis C Bauer

Abstract

Editing individual nucleotides is a crucial component for validating genomic disease association. It currently is 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 4-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 repair template (ssODN) to be a governing factor, where different regions of the ssODN have variable influence, which reflects the underlying biophysical mechanism. Our model improves HDR efficiency by 83% compared to traditionally chosen targets. Using our findings, we develop CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or -- for-the-first-time -- HDR-mediated nucleotide changes. CUNE can be run via the web at: https://gt-scan.net/cune .

Related Concepts

Base Sequence
Learning
Laboratory mice
Nucleotides
Universities
Site
Scanning
Research Study
Mitochondrial Genome Maintenance
Double-strand Break Repair via Homologous Recombination

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