DeepRNA-Reg: a deep-learning based approach for comparative analysis of CLIP experiments
Abstract
ABSTRACT DeepRNA-Reg employs advances in deep learning to enable high-fidelity comparative analysis of paired datasets of high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). In a HITS-CLIP experimental paradigm where Ago2 targeting is selectively perturbed via gene knock-out of a microRNA cluster, DeepRNA-Reg offers a superior prediction set when compared with the current best prescription for differential HITS-CLIP analysis. Furthermore, DeepRNA-Reg predictions adhered better to the ground-truth of RNA primary and secondary structural motifs that enable miRNA-mediated targeting of RNA. In the tested data sets, DeepRNA-Reg uncovered novel mediators in the mechanism of microRNA-mediated restraint of type-2 immunity in T-Helper 2 cells. In a comparative analysis, DeepRNA-Reg predictions show greater translatability across distinct biological milieux, offering prediction sets with wide applicability for investigators.
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