Messenger RNA Splicing Signals

Abstract

Abstract Splicing is a post‐transcriptional processing step in which intervening sequences (introns) are excised and coding sequences (exons) are ligated together to create the mature mRNA (messenger ribonucleic acid) molecule. It is a sequential process that is facilitated by the information in the RNA (ribonucleic acid) sequence (splicing regulatory elements/signals) and numerous RNA‐binding proteins (trans factors). Splicing occurs through two biochemical steps, and is catalysed by a large ribonucleoprotein known as the spliceosome. The spliceosomal machinery recognises the core splicing signals and assembles in a stepwise fashion on the premRNA molecule. These signals include the obligate 5′ splice site, 3′ splice site and branch site sequence, as well as enhancer and silencer sequences which functionally interact with RNA binding proteins. A current research application in the field uses computational approaches to study splicing signals and predict the effect of single nucleotide variations on message processing. Key Concepts Splicing is a post‐transcriptional process in which certain sections of RNA (known as introns) are removed and other sections (known as exons) are ligated together, producing the mature mRNA molecule. Splicing is catalysed by a large macromolecular machine known as the spliceosome. The major elements required for splicing include the 5′ splice site (5′ss), the 3′ splice site (3′ss), the polypyrimidine tract and the branch site. Most introns begin with a ‘GT’ sequence and end with an ‘AG’ sequence; however, there exists a class of intron with different boundary sequences that are recognised by a parallel spliceosome. Outside of these major elements, there are additional enhancer and suppressor signals that interact with RNA‐binding proteins (RBPs) and affect splicing outcome. Different combinations and positions of binding of RBPs can result in different splicing outcome. Scientists do not yet have a complete understanding of all of the different splicing signals, and this topic is an area of active research. Researchers are using computational machine learning approaches to predict the effects of sequence variation on splicing outcome. Researchers are also using new methods of sequencing to determine which RNA sequences the RBPs are recognising.