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.
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August 2016
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A schematic representation of TGF-β and BMP9 signalling in endothelial cells via their serine/threonine type I and type II receptors, co-receptor endoglin and downstream Smad intracellular proteins. Taken from the article ‘Targeting tumour vasculature by inhibiting activing receptor-like kinase (ALK)1 function’ by de Vinuesa et al. in this issue (volume 44, issue 4, pages 1142-1149). - PDF Icon PDF LinkTable of Contents
Review Article|
August 15 2016
Computational analysis of Ebolavirus data: prospects, promises and challenges
Martin Michaelis;
Martin Michaelis
1
*School of Biosciences, University of Kent, Canterbury, Kent, CT2 7NH, U.K.
1Correspondence may be addressed to either of these authors (email [email protected] or [email protected]).
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Jeremy S. Rossman;
Jeremy S. Rossman
*School of Biosciences, University of Kent, Canterbury, Kent, CT2 7NH, U.K.
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Mark N. Wass
Mark N. Wass
1
*School of Biosciences, University of Kent, Canterbury, Kent, CT2 7NH, U.K.
1Correspondence may be addressed to either of these authors (email [email protected] or [email protected]).
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Publisher: Portland Press Ltd
Received:
March 18 2016
Online ISSN: 1470-8752
Print ISSN: 0300-5127
© 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society
2016
Biochem Soc Trans (2016) 44 (4): 973–978.
Article history
Received:
March 18 2016
Citation
Martin Michaelis, Jeremy S. Rossman, Mark N. Wass; Computational analysis of Ebolavirus data: prospects, promises and challenges. Biochem Soc Trans 15 August 2016; 44 (4): 973–978. doi: https://doi.org/10.1042/BST20160074
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