Predicting Autonomous Promoter Activity Based on Genome-wide Modeling of Massively Parallel Reporter Data

Existing methods to systematically characterize sequence-intrinsic activity of promoters are limited by relatively low throughput and the length of sequences that could be tested. Here we present Survey of Regulatory Elements (SuRE), a method to assay more than a billion DNA fragments in parallel for their ability to drive transcription autonomously. In SuRE, a plasmid library is constructed of random genomic fragments upstream of a barcode and decoded by paired-end sequencing. This library is transfected into cells and transcribed barcodes are quantified in the RNA by high-throughput sequencing. By computationally analyzing the resulting data using generalized linear models, we succeed in delineating subregions within promoters that are relevant for their activity on a genomic scale, and making accurate predictions of expression levels that can be used to inform minimal promoter reporter construct design. We also show how our approach can be extended to analyze the differential impact of single-nucleotide polymorphisms (SNPs) on gene expression