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Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Fig 1

Integrated host factor prioritization from viral infection RNAi screening data using a two-stage procedure.

(A) We normalized and integrated data from RNAi perturbation screens of four different positive-sense RNA viruses. (B) Stage 1: We estimate pan-viral effects γ = {γ1, …, γG} from the integrated data sets for each of G genes using a random effects model and rank the genes by their absolute effect size. The gene effects represent the impact of a genetic knockdown of the life cycle on the entire group of viruses. (C) Stage 2: To account for genes that have not been knocked down in the RNAi screens, and to possibly account for false negatives in our rankings using biological prior knowledge, we map the gene effects γg onto a protein-protein interaction network. We then propagate the inferred estimates over the graph using network diffusion resulting in a final ranking of genes that are predicted to have a significant impact on the pan-viral replication cycle.

Fig 1