Abstract
RNA-binding proteins (RBPs) bind to their target RNA molecules by recognizing specific RNA sequences and structural contexts. The development of CLIP-seq and related protocols has made it possible to exhaustively identify RNA fragments that bind to RBPs. However, no efficient bioinformatics method exists to reveal the structural specificities of RBP–RNA interactions using these data. We present CapR, an efficient algorithm that calculates the probability that each RNA base position is located within each secondary structural context. Using CapR, we demonstrate that several RBPs bind to their target RNA molecules under specific structural contexts. CapR is available at https://sites.google.com/site/fukunagatsu/software/capr.
Highlights
RNA-binding proteins (RBPs) play integral roles in various post-transcriptional regulatory processes, including the splicing, processing, localization, degradation and translation of RNA molecules [1]
Datasets and methods used in the Cross-linking immunoprecipitation (CLIP)-seq data analysis Because it was shown that CapR is accurate in calculating structural profiles of RNA molecules, we applied it to several CLIP-seq datasets to reveal the structural specificities of RBP–RNA interactions
Specific RNA structural contexts recognized by RNA-binding proteins We investigated the preferred RNA structural contexts for each RBP and revealed that most RBPs prefer a specific structural context (Figure 4 and Additional file 1: Figure S3)
Summary
RNA-binding proteins (RBPs) play integral roles in various post-transcriptional regulatory processes, including the splicing, processing, localization, degradation and translation of RNA molecules [1]. CLIP-seq methods bond an RBP and RNAs covalently by ultraviolet crosslinking, collect them by immunoprecipitation and directly sequence the RBP-bound sites of the RNAs. In contrast, CLIP-seq methods bond an RBP and RNAs covalently by ultraviolet crosslinking, collect them by immunoprecipitation and directly sequence the RBP-bound sites of the RNAs Using these technologies, researchers can identify sequential RNA motifs that are over-represented around the binding sites of each RBP using bioinformatics methods similar to those used for analyzing transcription-factor binding DNA motifs [9]. Researchers can identify sequential RNA motifs that are over-represented around the binding sites of each RBP using bioinformatics methods similar to those used for analyzing transcription-factor binding DNA motifs [9] Such sequential motifs are often very short (up to ten bases), and there are many unbound sites that have the same motif. Sequential motifs alone cannot explain the specificity of RBP–RNA interactions
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