Abstract
Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug-gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of "undruggable" proteins, and context-specific rewiring of genetic networks.
Highlights
20 yr removed from the first draft of the human genome, our understanding of how genes function together to form cellular and organismal networks is still growing rapidly
We sought to quantify the extent to which locus bias affects CRISPR-based coessentiality estimates for individual genes across the genome and develop a preprocessing approach that mitigates this source of error
To determine the expected rate of syntenic coessentiality, we considered two null distributions: the first attributable to chance based on the number of genes on each chromosome and the second from coessentiality analysis performed with data from 712 shRNA genetic screens, which do not produce a DNA damage phenotype when targeting copy number–variable genes (“RNAi”) (McFarland et al, 2018)
Summary
20 yr removed from the first draft of the human genome, our understanding of how genes function together to form cellular and organismal networks is still growing rapidly. Coessentiality analysis is based on the observation that the importance of a given gene to cellular growth (or any other phenotype) depends on cellular context (Rancati et al, 2018). The observation that strong genetic fitness correlations are predictive of participation in the same biological process has already spurred discoveries of novel gene functions from publicly available, genome-scale fitness screening datasets (McDonald et al, 2017; Wang et al, 2017; Boyle et al, 2018; Pan et al, 2018; Rauscher et al, 2018; Kim et al, 2019; Wainberg et al, 2019 Preprint; Bayraktar et al, 2020)
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