MPRAnalyze: statistical framework for massively parallel reporter assays

Massively parallel reporter assays (MPRAs) are a technique that enables testing thousands of regulatory DNA sequences and their variants in a single, quantitative experiment. Despite growing popularity, there is lack of statistical methods that account for the different sources of uncertainty inherent to these assays, thus effectively leveraging their promise. Development of such methods could help enhance our ability to identify regulatory sequences in the genome, understand their function under various setting, and ultimately gain a better understanding of how the regulatory code and its alteration lead to phenotypic consequence. Here we present MPRAnalyze: a statistical framework dedicated to analyzing MPRA count data. MPRAnalyze addresses the major questions that are posed in the context of MPRA experiments: estimating the magnitude of the effect of a regulatory sequence in a single condition setting, and comparing differential activity of regulatory sequences across multiple conditions. The framework uses a nested construction of generalized linear models to account for uncertainty in both DNA and RNA observations, controls for various sources of unwanted variation, and incorporates negative controls for robust hypothesis testing, thereby providing clear quantitative answers in complex experimental settings. We demonstrate the robustness, accuracy and applicability of MPR-Analyze on simulated data and published data sets and compare it against the existing analysis methodologies. MPRAnalyze is implemented as an R package and is publicly available through Bioconductor [1].