PiCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction.

ABSTRACT: CRISPR/Cas programmable nuclease systems have become ubiquitous in the field of gene editing. With progressing development, applications in in vivo therapeutic gene editing are increasingly within reach, yet limited by possible adverse side effects from unwanted edits. Recent years have thus seen continuous development of off-target prediction algorithms trained on in vitro cleavage assay data gained from immortalised cell lines. It has been shown that in contrast to experimental epigenetic features, computed physically informed features are so far underutilised despite bearing considerably larger correlation with cleavage activity. Here, we implement state-of-the-art deep learning algorithms and feature encodings for off-target prediction with emphasis on physically infor

SUBMITTER: Stortz F 

PROVIDER: S-EPMC10316064 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature