Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Computational Methods for Predicting Functions at The mRNA Isoform Level

Version 1 : Received: 13 July 2020 / Approved: 20 July 2020 / Online: 20 July 2020 (10:53:23 CEST)

A peer-reviewed article of this Preprint also exists.

Mishra, S.K.; Muthye, V.; Kandoi, G. Computational Methods for Predicting Functions at the mRNA Isoform Level. Int. J. Mol. Sci. 2020, 21, 5686. Mishra, S.K.; Muthye, V.; Kandoi, G. Computational Methods for Predicting Functions at the mRNA Isoform Level. Int. J. Mol. Sci. 2020, 21, 5686.

Journal reference: Int. J. Mol. Sci. 2020, 21, 5686
DOI: 10.3390/ijms21165686

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, such as the apoptosis associated CASP3 gene, can have dramatically different functions. The shorter mRNA isoform product CASP3-S inhibits apoptosis, while the longer CASP3-L mRNA isoform promotes apoptosis. 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.

Subject Areas

Alternative Splicing; RNA-Seq; Machine Learning; Deep Learning; Recommender Systems; Multiple Instance Learning; mRNA Isoforms; Gene Ontology

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.