Computational Methods for Predicting Functions at the mRNA Isoform Level

Abstract

Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.

An expectation–maximization framework for comprehensive prediction of isoform-specific functions

“…This in turn opens the door to the development of novel isoform-specific function prediction methods. Nevertheless, it remains challenging to predict GO terms at isoform level using supervised methods, due to our limited knowledge about isoform functions that precludes the application of powerful supervised machine-learning methods ( Bhuiyan et al 2018 ; Mishra et al 2020 ).…”

“…A major roadblock is the lack of experimentally confirmed functional annotations of isoforms ( Bhuiyan et al 2018 ). For this reason, a number of methods have been developed for isoform-specific function prediction ( Mishra et al 2020 ). Many existing methods leverage multiple-instance learning ( Eksi et al 2013 ; Li et al 2014a,b ; Luo et al 2017 ; Shaw et al 2018 ; Chen et al 2019 ).…”

Alternative Splicing of Pre-mRNA in the Control of Immune Activity

“…(1) Cis regulation: cis -elements for splicing refer to the sequences in the pre-mRNA that correlate with the modulation of splicing, which include the GU sequence at the 5′ end as the splice donor site, the AG sequence at the 3′ end as the splice acceptor site, and the adenine branch point for definition of the exon and intron by the spliceosome. Also included are exonic splicing enhancers, exonic splicing silencers, intronic splicing enhancers, and intronic splicing silencers [ 4 , 73 , 74 ]. Alternative exons are generally associated with weak splice site signals [ 75 ].…”