Luca Crepaldi

The paper presents a genome-scale CRISPRi perturbation map in human pluripotent stem cells using single-cell RNA sequencing to study trans gene regulation across diverse genetic backgrounds.

The paper investigates how DNA repair genes influence Cas9-induced mutations by analyzing mutational outcomes across 2,838 synthetic targets in mouse cells, revealing gene-specific roles and improving predictive models.

This paper identifies position-specific sequence determinants of base editing efficiency and develops a machine learning model to predict editing outcomes for adenine and cytosine editors.

This paper introduces a minimized double guide RNA library for CRISPR/Cas9 screens, reducing scale by fivefold while maintaining performance.

The paper predicts Cas9-induced mutation outcomes based on surrounding DNA sequence using data from over 1 billion mutational outcomes.