Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/131035
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Type: | Journal article |
Title: | Applications of deep mutational scanning in virology |
Author: | Burton, T.D. Eyre, N.S. |
Citation: | Viruses, 2021; 13(6):1020-1020 |
Publisher: | MDPI AG |
Issue Date: | 2021 |
ISSN: | 1999-4915 1999-4915 |
Statement of Responsibility: | Thomas D. Burton and Nicholas S. Eyre |
Abstract: | Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research. |
Keywords: | Deep mutational scanning; virology; virus; hepatitis; Zika; Dengue; Influenza; SARS-CoV-2 |
Rights: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
DOI: | 10.3390/v13061020 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1163662 |
Published version: | http://dx.doi.org/10.3390/v13061020 |
Appears in Collections: | Aurora harvest 8 Molecular and Biomedical Science publications |
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hdl_131035.pdf | 2.31 MB | Adobe PDF | View/Open |
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