Version 1
: Received: 12 January 2023 / Approved: 16 January 2023 / Online: 16 January 2023 (01:12:34 CET)
Version 2
: Received: 12 April 2023 / Approved: 13 April 2023 / Online: 13 April 2023 (03:07:07 CEST)
Following the central dogma of molecular biology, gene expression variability can aid in predicting and explaining the wide variety of protein products, functions, and, ultimately,variability in phenotypes. There is currently overlapping terminology used to describe the types of variability in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptional diversity as quantifying transcriptional changes as a measure of the variability in 1) the total expression of all genes or a gene across samples (transcriptome diversity) or 2) the isoform-specific expression of a given gene (isoform diversity). We first overview modulators and quantification of gene expression variability. Then, we discuss the role alternative splicing plays in driving transcript isoform expression variability and how isoform diversity can be quantified. Additionally, we overview computational resources for calculating transcriptome and isoform diversity for short- and long-read sequencing data. Finally, we discuss future applications of transcriptional diversity. This review provides a comprehensive overview of how gene expression variability arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms, and species
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
