Abstract
Background
Our understanding of post-transcriptional gene regulation has increased exponentially with the development of robust methods to define protein-RNA interactions across the transcriptome. In this review, we highlight the evolution and successful applications of crosslinking and immunoprecipitation (CLIP) methods to interrogate protein-RNA interactions in a transcriptome-wide manner.
Results
Here, we survey the vast array of in vitro and in vivo approaches used to identify protein-RNA interactions, including but not limited to electrophoretic mobility shift assays, systematic evolution of ligands by exponential enrichment (SELEX), and RIP-seq. We particularly emphasize the advancement of CLIP technologies, and detail protocol improvements and computational tools used to analyze the output data. Importantly, we discuss how profiling protein-RNA interactions can delineate biological functions including splicing regulation, alternative polyadenylation, cytoplasmic decay substrates, and miRNA targets.
Conclusions
In summary, this review summarizes the benefits of characterizing RNA-protein networks to further understand the regulation of gene expression and disease pathogenesis. Our review comments on how future CLIP technologies can be adapted to address outstanding questions related to many aspects of RNA metabolism and further advance our understanding of RNA biology.
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This work was supported by funds from the National Institutes of Health to LLZ (T32 GM08056) and DDL (R01 GM107331), USA.
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Author summary: Rapidly advancing sequencing methodologies continue to improve our understanding of gene regulation and disease pathogenesis. An emerging area of interest is the contribution of RNA regulation in development and disease. In this review, we highlight techniques widely used to interrogate RNA regulatory networks that fine tune gene expression and how they impact cell biology.
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Hannigan, M.M., Zagore, L.L. & Licatalosi, D.D. Mapping transcriptome-wide protein-RNA interactions to elucidate RNA regulatory programs. Quant Biol 6, 228–238 (2018). https://doi.org/10.1007/s40484-018-0145-6
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DOI: https://doi.org/10.1007/s40484-018-0145-6