Prober: A general toolkit for analyzing sequencing-based ‘toeprinting’ assays

Abstract

A number of high-throughput transcriptase drop-off assays have recently been developed to probe post-transcriptional dynamics of RNA-protein interaction, RNA structure, and post-transcriptional modifications. Although these assays survey a diverse set of epitranscriptomic marks, they share methodological similarities and as such their interpretation is predicated on addressing similar computational challenges. Among these, a key question is how to learn isoform-specific chemical modification profiles in the face of complex read multi-mapping. In this paper, we propose PROBer, the first rigorous statistical model to handle these challenges for a general set of sequencing-based toeprinting assays.