Cell-type specific prediction of RNA stability from RNA-protein interactions

Abstract

RNA-binding proteins (RBPs) are important contributors to post-transcriptional regulatory processes. The combinatorial action of expressed RBPs and non-coding factors bound to the same transcript determines post-transcriptional properties of the mRNA in a context-dependent manner. To gain a better understanding of RNA stability and translational activity across different conditions, we have compiled and analyzed a set of ribosome profiling datasets for four human cell lines and used existing and newly generated metabolic labeling data to determine matching RNA degradation rates. We then used machine learning methods to predict RNA degradation rate and translation level from RBP binding information, which comprised existing in vivo binding datasets and computationally predicted binding sites. Utilizing this new RNA stability resource, we predicted RNA degradation rate and translation level from RBP binding alone. In vivo binding sites had higher importance for prediction compared to computationally predicted binding sites, likely due to confounding effects. We further explored the feature importance of different RBPs for stability prediction in the context of differential stability conferred by 3UTR isoforms. Taken together, an RNA stability machine learning model trained on one context successfully generalizes but is impacted by the availability and reliability of current data.