CN3-PNRR

CRISPR-based screening approaches suffer from the loss of genotype-phenotype data of gRNAs that do not edit their targets efficiently. Therfore, CRISPR-based screening approaches would benefit from methods and criteria to prioritize effective gRNAs prior to the screen. In this project, we will generate large-scale functional datasets using CRISPR base editors in high-throughput screening. These data will be used to rank gRNAs based on efficacy and specificity through machine learning, with the aim of enabling improved library design. In parallel, we will use base editing to introduce single-nucleotide variants in RNA modification effectors, with a focus on the m6A pathway. This approach will enable the identification of variants that support cancer cell proliferation and drug resistance.