A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP data

Understanding post-transcriptional gene regulation is a key challenge in today's biology. The new technologies of RNAcompete and RNA Bind-n-Seq enable the measurement of the binding intensities of one RNA-binding protein (RBP) to numerous synthetic RNA sequences in a single experiment. Recently, Van Nostrand et al. reported the results of RNA Bind-n-Seq experiments measuring binding of 78 human RBPs. Because 31 of these RBPs were also covered by RNAcompete technology, a large-scale comparison between implementations of these two in vitro technologies is now possible. Here, we assessed the similarities and differences between binding models, represented as a list of k-mer scores, inferred from RNAcompete and RNA Bind-n-Seq, and also measured how well these models predict in vivo binding. Our results show that RNA Bind-n-Seq- A nd RNAcompete-derived models agree (Pearson correlation > 0.5) for most RBPs (23 out of 31). RNA Bind-n-Seq-derived k-mer scores predict RNAcompete binding measurements quite well (average Pearson correlation 0.26), and both technologies produce k-mer scores that achieve comparable results in predicting in vivo binding (average AUC 0.7). When inspecting RNA structural preferences inferred from the data of RNA Bind-n-Seq and RNAcompete, we observed high concordance in binding preferences. Through our study, we developed a new k-mer score for RNA Bind-n-Seq and extended it to include RNA structural preferences.