Nov 21, 2018

Computational approaches for the analysis of RNA-protein interactions: A primer for biologists

The Journal of Biological Chemistry
Kat S Moore, Peter A C 't Hoen

Abstract

RNA-binding proteins (RBPs) play important roles in the control of gene expression and the coordination of different layers of post-transcriptional regulation. Interactions between certain RBPs and mRNA transcripts are notoriously difficult to predict, as any given protein-RNA interaction may rely not only on RNA sequence, but also on three-dimensional RNA structures, competitive inhibition from other RBPs, and input from cellular signaling pathways. Advanced and high-throughput technologies for the identification of RNA-protein interactions have come to the rescue, but the identification of binding sites and downstream functional effects of RBPs from the resulting data can be challenging. In this review, we discuss statistical inference and machine-learning approaches and tools relevant for the study of RBPs and the analysis of large-scale RNA-protein interaction datasets. This primer is intended for life scientists who are interested in incorporating these tools into their own research. We begin with the demystification of regression models, as used in the analysis of next-generation sequencing data, and progress to a discussion of Hidden Markov Models, which are of particular value in analyzing cross-linking followed by immu...Continue Reading

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Citations

Mentioned in this Paper

In Silico
Study
Biochemical Pathway
Research
Post-Transcriptional Regulation
Trees (plant)
High Throughput Screening
Gene Expression
RNA-Protein Interaction
Massively-Parallel Sequencing

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