Computational Methods for Predicting Functions at the mRNA Isoform Level

• Published in: International journal of molecular sciences 2020-08, Vol.21 (16), p.5686
• Main Authors: Mishra, Sambit K, Muthye, Viraj, Kandoi, Gaurav
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative splicing; Alternative Splicing - genetics; Animals; Annotations; Computational Biology - methods; Computer applications; Datasets; Deep learning; Environmental stress; Experiments; Functionals; Gene expression; Gene Regulatory Networks; Genomes; Humans; Isoforms; Learning algorithms; Machine Learning; multiple instance learning; Ontology; Proteins; Recommender systems; Review; RNA Isoforms - genetics; RNA Isoforms - metabolism; RNA, Messenger - genetics; RNA, Messenger - metabolism; RNA-seq; Splicing
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms21165686

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.