Splicing Code Modeling
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How do cis and trans elements involved in pre-mRNA splicing come together to form a splicing “code”? This question has been a driver of much of the research involving RNA biogenesis. The variability of splicing outcome across developmental stages and between tissues coupled with association of splicing defects with numerous diseases highlights the importance of such a code. However, the sheer number of elements involved in splicing regulation and the context-specific manner of their operation have made the derivation of such a code challenging. Recently, machine learning-based methods have been developed to infer computational models for a splicing code. These methods use high-throughput experiments measuring mRNA expression at exonic resolution and binding locations of RNA-binding proteins (RBPs) to infer what the regulatory elements that control the inclusion of a given pre-mRNA segment are. The inferred regulatory models can then be applied to genomic sequences or experimental conditions that have not been measured to predict splicing outcome. Moreover, the models themselves can be interrogated to identify new regulatory mechanisms, which can be subsequently tested experimentally. In this chapter, we survey the current state of this technology, and illustrate how it can be applied by non-computational or RNA splicing experts to study regulation of specific exons by using the AVISPA web tool.
KeywordsSplicing code Posttranscriptional regulation Alternative splicing Machine learning Computational biology
The authors would like to thank Matthew Gazzara and Alex Amlie-Wolf for helpful comments and suggestions regarding the manuscript.
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