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Trends Microbiol. 2017 Oct 9. pii: S0966-842X(17)30206-8. doi: 10.1016/j.tim.2017.09.001. [Epub ahead of print]
In Silico Vaccine Strain Prediction for Human Influenza Viruses.
Klingen TR1, Reimering S1, Guzm?n CA2, McHardy AC3.
Author information
Abstract
Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.
Copyright ? 2017 Elsevier Ltd. All rights reserved.
KEYWORDS:
GISRS; computational predictions; influenza viruses; vaccine; viral evolution
PMID: 29032900 DOI: 10.1016/j.tim.2017.09.001
In Silico Vaccine Strain Prediction for Human Influenza Viruses.
Klingen TR1, Reimering S1, Guzm?n CA2, McHardy AC3.
Author information
Abstract
Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.
Copyright ? 2017 Elsevier Ltd. All rights reserved.
KEYWORDS:
GISRS; computational predictions; influenza viruses; vaccine; viral evolution
PMID: 29032900 DOI: 10.1016/j.tim.2017.09.001