Stanley Gill

TL;DR: DEMETER, an analytical framework that segregates on- from off-target effects of RNAi, demonstrates the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC and provides a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

TL;DR: It is found that the number of CRISPR/Cas9-induced DNA breaks dictates a gene-independent antiproliferative response in cells, which has practical implications for using CRISpr/cas9 to interrogate cancer gene function and illustrate that cancer cells are highly sensitive to site-specific DNA damage, which may provide a path to novel therapeutic strategies.

TL;DR: It is concluded that CRISPRc and CRISpri have different off-target effects and combining these approaches provides complementary information in loss-of-function genetic screens.

TL;DR: Simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost, and can be used to add cell health annotations to Cell Painting datasets.

TL;DR: Findings reveal a differential dependency of clear cell ovarian cancers on EGLN1, thus identifying E GLN1 as a potential therapeutic target in clear cell Ovarian cancer patients.