Biochemical and bioinformatic methods for elucidating the role of RNA-protein interactions in posttranscriptional regulation

• Published in: Briefings in functional genomics 2015-03, Vol.14 (2), p.102-114
• Main Authors: Kloetgen, Andreas, Münch, Philipp C, Borkhardt, Arndt, Hoell, Jessica I, McHardy, Alice C
• Format: Article
• Language: English
• Subjects: Base Sequence; Computational Biology - methods; Databases, Genetic; Gene Expression Regulation; Molecular Sequence Data; RNA - metabolism; RNA-Binding Proteins - metabolism; Software; Transcription, Genetic
• ISSN: 2041-2657; 2041-2649; 2041-2657
• DOI: 10.1093/bfgp/elu020

Our understanding of transcriptional gene regulation has dramatically increased over the past decades, and many regulators of gene expression, such as transcription factors, have been analyzed extensively. Additionally, in recent years, deeper insights into the physiological roles of RNA have been obtained. More precisely, splicing, polyadenylation, various modifications, localization and the translation of messenger RNAs (mRNAs) are regulated by their interaction with RNA-binding proteins (RBPs). New technologies now enable the analysis of this regulation at different levels. A technique known as ultraviolet (UV) cross-linking and immunoprecipitation (CLIP) allows us to determine physical protein-RNA interactions on a genome-wide scale. UV cross-linking introduces covalent bonds between interacting RBPs and RNAs. In combination with immunoprecipitation and deep sequencing techniques, tens of millions of short reads (representing bound RNAs by an RBP of interest) are generated and are used to characterize the regulatory network mediated by an RBP. Other methods, such as mass spectrometry, can also be used for characterization of cross-linked RBPs and RNAs instead of CLIP methods. In this review, we discuss experimental and computational methods for the generation and analysis of CLIP data. The computational methods include short-read alignment, annotation and RNA-binding motif discovery. We describe the challenges of analyzing CLIP data and indicate areas where improvements are needed.