Skipper analysis of eCLIP datasets enables sensitive detection of constrained translation factor binding sites

Abstract

Technology for crosslinking and immunoprecipitation (CLIP) followed by sequencing (CLIP-seq) has identified the transcriptomic targets of hundreds of RNA-binding proteins in cells. To increase the power of existing and future CLIP-seq datasets, we introduce Skipper, an end-to-end workflow that converts unprocessed reads into annotated binding sites using an improved statistical framework. Compared with existing methods, Skipper on average calls 210%-320% more transcriptomic binding sites and sometimes >1,000% more sites, providing deeper insight into post-transcriptional gene regulation. Skipper also calls binding to annotated repetitive elements and identifies bound elements for 99% of enhanced CLIP experiments. We perform nine translation factor enhanced CLIPs and apply Skipper to learn determinants of translation factor occupancy, including transcript region, sequence, and subcellular localization. Furthermore, we observe depletion of genetic variation in occupied sites and nominate transcripts subject to selective constraint because of translation factor occupancy. Skipper offers fast, easy, customizable, and state-of-the-art analysis of CLIP-seq data.