MPRAudit Quantifies the Fraction of Variance Described by Unknown Features in Massively Parallel Reporter Assays

Abstract

Transformative advances in molecular technologies, such as massively parallel reporter assays (MPRAs) and CRISPR screens, can efficiently characterize the effects of genetic and genomic variation on cellular phenotypes. Analysis approaches to date have focused on identifying individual genomic regions or genetic variants that perturb a phenotype of interest. In this work, we develop a wholistic framework (MPRAudit) to determine the global contribution of sequence to phenotypic variation across subsets of the entire experiment, opening the door to myriad novel analyses. For example, MPRAudit can reliably estimate the upper limit of predictive performance, the fraction of variation attributed to specific biological categories, and the total contribution of experimental noise. We demonstrate through simulation and application to several types of real MPRA data sets how MPRAudit can lead to an improved understanding of experimental quality, molecular biology, and guide future research. Applying MPRAudit to real MPRA data, we observe that sequence variation is the primary driver of outcome variability, but that known biological categories explain only a fraction of this variance. We conclude that our understanding of how sequence variation impacts phenotype, even at the level of MPRAs, remains open to further scientific discovery.