Fig. 1: A genome-wide screen of the SARS-CoV-2 genome by Sens.AI.

a Oligo design. Each oligo had a unique RNAi trigger in the context of a miR-30 backbone along with a 50 nucleotide stretch flanking the viral RNAi target site; b Library design. Each plasmid contained a specific RNAi trigger and its matching target site in cis as part of the 3’UTR of a Venus reporter; c–e Scheme of an in vitro screen in human cells: c The RNAi machinery was first turned off. Dicer^−/− 293FT cells were infected with the plasmid library and sorted for Venus^high expression, which formed the T₀ read out; d The RNAi machinery was then turned on. We restored the RNAi machinery by ectopic expression of a destabilised Dicer (ddDicer); e The activity of the RNAi machinery was modulated quantitatively. Two biological replicates were subjected to seven different conditions, designed to titer Dicer expression either downward, by treatment with an anti-Dicer siRNA, or upwards, by treatment with Shield-1. Upon FACS sorting, Venus^low and Venus^dark cells were collected, and their oligo constructs regions were sequenced; f The resulting data matrix of the screen. Each row represents a specific combination of a treatment condition (the far left column) and a FACS gate (denoted as D, for Dark, and L, for Low). These row vectors describe the enrichment of each tested RNAi trigger (blue) compared to negative (green highlighted) and positive (red highlighted) controls. The area under the curve (AUC) column was calculated based on the ability to distinguish the positive from the negative controls; g Selection of the two best conditions. The two-row vectors with the highest AUC were selected. Precision-recall (left) and receiver operating characteristics (right) curves calculated for the intrinsic controls are shown. The screen score of each RNAi trigger was calculated as the average of these two-row vectors.