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DeepRNA-Reg: A Deep-Learning Based Approach for Comparative Analysis of CLIP Experiments

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 activity is precisely perturbed via gene knock-out of a microRNA cluster, DeepRNA-Reg offers a superior prediction set than the current best prescription for differential HITS-CLIP; furthermore, DeepRNA-Reg predictions adhere better to the ground-truth of the RNA primary and secondary structural motifs that enable miRNA-mediated targeting of RNA. 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE273503 | GEO | 2024/08/01

REPOSITORIES: GEO

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