Inference of Transcriptional Regulation From STARR-seq Data

Transcription, a key regulatory process, involves RNA polymerase II (Pol-II) transcribing DNA into RNA, regulated by transcription factors (TFs) binding to DNA at enhancer elements. Measuring the enhancer activity of large libraries of distinct DNA sequences is now possible using Massively Parallel Reporter Assays (MPRAs) such as STARR-seq, and computational methods have been developed to identify the dominant statistical patterns of TF binding within these large datasets. Such methods are global in their approach and may overlook important regulatory sites which function only within the local context. Here we introduce a method for inferring regulatory sites within an enhancer sequence based on measurements of its transcriptional activity. The model is based on a mean-field thermodynamic description of Pol-II binding that includes interactions with bound TFs. Our method applied to simulated STARR-seq data for a variety of enhancers shows how data quality impacts the inference and also how it can find local regulatory sites that may be missed in a global approach. We also apply the method to recently measured STARR-seq data on androgen receptor (AR) bound sequences, a TF that plays an important role in the regulation of prostate cancer.