A method for benchmarking genetic screens reveals a predominant mitochondrial bias. Issue 5 (20th May 2021)

• Record Type: Journal Article
• Authors: Rahman, Mahfuzur; Billmann, Maximilian; Costanzo, Michael; Aregger, Michael; Tong, Amy H Y; Chan, Katherine; Ward, Henry N; Brown, Kevin R; Andrews, Brenda J; Boone, Charles; Moffat, Jason; Myers, Chad L
• Is Part Of: Molecular systems biology. Volume 17:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-20
• Subjects: computational evaluation -- CRISPR screens -- electron transport chain; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202010013
• Languages: English
• ISSNs: 1744-4292

Abstract

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. SYNOPSIS: FLEX is a method for systematic evaluation and benchmarking of large‐scale genetic datasets that measures both the quantity and the composition of functional signals in gene‐pair data. FLEX allows users to measure the predictive performance of functional networks against several annotation standards. FLEX provides information about the diversity of functional modules captured by the input data. Application of FLEX to co‐essentiality networks derived from DepMap CRISPR screens reveals a major functional bias for mitochondrial complexes. Differential phenotypes for ETC‐related genes in CRISPR screens may reflect differences in the effective sampling time, cell line doubling rate, and protein stability. Abstract : FLEX is a method for systematic evaluation and benchmarking of large‐scale genetic datasets that measures both the quantity and the composition of functional signals in gene‐pair data.