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A method for benchmarking genetic screens reveals a predominant mitochondrial bias |
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| Authors: | Mahfuzur Rahman Maximilian Billmann Michael Costanzo Michael Aregger Amy H Y Tong Katherine Chan Henry N Ward Kevin R Brown Brenda J Andrews Charles Boone Jason Moffat Chad L Myers |
| Affiliation: | 1. Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis MN, USA ; 2. Donnelly Centre, University of Toronto, Toronto ON, Canada ; 3. Bioinformatics and Computational Biology Graduate Program, University of Minnesota – Twin Cities, Minneapolis MN, USA ; 4. Department of Molecular Genetics, University of Toronto, Toronto ON, Canada |
| Abstract: | We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them. |
| Keywords: | computational evaluation CRISPR screens electron transport chain |