CLIP-seq methods allow the generation of genome-wide maps of RNA binding protein - RNA interaction sites. However, due to differences between different CLIP-seq assays, existing computational approaches to analyze the data can only be applied to a subset of assays. Here, we present a probabilistic model called omniCLIP that can detect regulatory elements in RNAs from data of all CLIP-seq assays. omniCLIP jointly models data across replicates and can integrate background information. Therefore, omniCLIP greatly simplifies the data analysis, increases the reliability of results and paves the way for integrative studies based on data from different assays.
Keywords: Bioinformatics; CLIP-seq; Generalized linear models; HITS-CLIP; Machine learning; Mixture models; PAR-CLIP; Protein-RNA interactions; eCLIP; iCLIP.