Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis.

ABSTRACT: Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that exp

SUBMITTER: Liu X 

PROVIDER: S-EPMC8041805 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature