Abstract
Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences’ activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.
Highlights
Parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment
Massively parallel reporter assays (MPRAs) data is produced from two parallel procedures: rthe vector of latent transcript counts (RNA)-sequencing is used to measure the number of transcripts produced from each barcode, and DNAsequencing is used to measure the number of construct copies of each barcode
Both DNA and RNA measurement procedures provide an approximate and noisy estimation, an issue exacerbated by the unstable nature of a ratio: minor differences in the counts themselves can result in major shifts in the ratio, especially when dealing with small numbers
Summary
Parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. We present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences’ activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods
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