Bayesian Hidden Markov Models to Identify RNA-Protein Interaction Sites in PAR-CLIP

Abstract

The photoactivatable ribonucleoside enhanced cross-linking immunoprecipitation (PAR-CLIP) has been increasingly used for the global mapping of RNA-protein interaction sites. There are two key features of the PAR-CLIP experiments: The sequence read tags are likely to form an enriched peak around each RNA-protein interaction site; and the cross-linking procedure is likely to introduce a specific mutation in each sequence read tag at the interaction site. Several ad hoc methods have been developed to identify the RNA-protein interaction sites using either sequence read counts or mutation counts alone; however, rigorous statistical methods for analyzing PAR-CLIP are still lacking. In this study, we propose an integrative model to establish a joint distribution of observed read and mutation counts. To pinpoint the interaction sites at single base-pair resolution, we developed a novel modeling approach that adopts non-homogeneous hidden Markov models to incorporate the nucleotide sequence at each genomic location. Both simulation studies and data application showed that our method outperforms the ad hoc methods, and provides reliable inferences for the RNA-protein binding sites from PAR-CLIP data.