Jolanda S. van Leeuwen

The paper identifies suppressor variants that alter gene essentiality across yeast strains, revealing context-dependent genetic mechanisms.

The paper develops a method to quantify genetic interactions from CRISPR-Cas9 screens and identifies systematic variations in screen data.

The paper constructs a genome-scale genetic interaction network from 4 million double mutants in HAP1 cells, revealing conserved genetic network principles across species and uncovering novel gene functions and cancer dependencies.

The paper shows that genetic suppression interactions are largely conserved across different yeast genetic backgrounds, suggesting potential for therapeutic applications.

The paper examines dispensable essential genes and their association with bypass suppressor mutations across species, revealing distinct phylogenetic properties and evolutionary implications.

The paper identifies suppressor mutations that rescue human genetic defects by analyzing 2,400 papers and predicting suppressor genes in the human genome.

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.

The paper measures how natural genetic variation can suppress mutations in essential yeast genes, finding that 35% of tested genes can be suppressed by modifier genes in different backgrounds.

This study develops a yeast-based chemical-genetic screening system to annotate compound functionality across biological processes and identifies compounds with dual modes of action.

The paper evaluates CRISPR/Cas9 knockout screens in human cell lines and designs an optimized gRNA library to improve gene knockout efficiency and accuracy.