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Computational analysis of Ebolavirus data: prospects, promises and challenges

Michaelis, Martin, Rossman, Jeremy S, Wass, Mark N. (2016) Computational analysis of Ebolavirus data: prospects, promises and challenges. Biochemical Society transactions, 44 (4). pp. 973-8. ISSN 1470-8752. (doi:10.1042/BST20160074) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1042/BST20160074

Abstract

The ongoing Ebola virus (also known as Zaire ebolavirus, a member of the Ebolavirus family) outbreak in West Africa has so far resulted in >28000 confirmed cases compared with previous Ebolavirus outbreaks that affected a maximum of a few hundred individuals. Hence, Ebolaviruses impose a much greater threat than we may have expected (or hoped). An improved understanding of the virus biology is essential to develop therapeutic and preventive measures and to be better prepared for future outbreaks by members of the Ebolavirus family. Computational investigations can complement wet laboratory research for biosafety level 4 pathogens such as Ebolaviruses for which the wet experimental capacities are limited due to a small number of appropriate containment laboratories. During the current West Africa outbreak, sequence data from many Ebola virus genomes became available providing a rich resource for computational analysis. Here, we consider the studies that have already reported on the computational analysis of these data. A range of properties have been investigated including Ebolavirus evolution and pathogenicity, prediction of micro RNAs and identification of Ebolavirus specific signatures. However, the accuracy of the results remains to be confirmed by wet laboratory experiments. Therefore, communication and exchange between computational and wet laboratory researchers is necessary to make maximum use of computational analyses and to iteratively improve these approaches.

Item Type: Article
DOI/Identification number: 10.1042/BST20160074
Subjects: Q Science
Divisions: Faculties > Sciences > School of Biosciences
Depositing User: Mark Wass
Date Deposited: 20 Dec 2018 12:58 UTC
Last Modified: 04 Sep 2019 15:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/71378 (The current URI for this page, for reference purposes)
Michaelis, Martin: https://orcid.org/0000-0002-5710-5888
Wass, Mark N.: https://orcid.org/0000-0001-5428-6479
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