A CRISPR-Cas9 puzzle revealed by machine learning

The paper in Nature Communications is here: https://go.nature.com/2rKKfAQ During my postdoc in the laboratory of Luciano Marraffini at the Rockefeller University I had the chance to work on the early developments of CRISPR-Cas9 technologies and in particular their application to modify the genome of bacteria or control gene expression. The catalytically dead variant of Cas9 known as dCas9 can be programmed to bind almost any gene of interest, but it just sits on the DNA instead of introducing a break as Cas9 would. Binding of dCas9 to DNA is strong enough to block the RNA polymerase when in the proper orientation (the guide must bind to the coding strand of the target gene), and the ease with which it can be reprogrammed makes it a fantastic tool to study the effect of silencing genes. When I started my group at the Institut Pasteur 4 years ago, one of my first goal was to setup a genome-wide screen to exploit the properties of dCas9 to investigate the function of genes in E. coli in a systematic way.