Abstract
A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for analysis of influenza viruses which could be used as an efficient tool for selection of vaccine viruses, assessment of the effectiveness of seasonal influenza vaccines, and prediction of the epidemic/pandemic potential of novel influenza viruses.
Highlights
Vaccination is the most effective way to prevent infection with seasonal influenza viruses
We analyzed the hemagglutinin HA1 region from (i) 2,379 H3N2 viruses collected in Europe and North America from January 2014 to February 2015, (ii) 804 human and animal H3N2 viruses collected in the United States (US) from January to August 2015 that were stored in GISAID (GISAID database1 and (iii) vaccine viruses A/Texas/50/2012 and A/Switzerland/9715293/2013 for 2014/2015 and 2015/2016 flu season, respectively
The main weaknesses of the multiple sequence alignment (MSA)-based phylogenetic algorithms for analysis of protein sequences are (i) insensitivity to position of the mutations, (ii) failure to consider deletion within sequence, and (iii) lack of the information about biological effect of mutations
Summary
Vaccination is the most effective way to prevent infection with seasonal influenza viruses. Evolution of influenza viruses between the time of vaccine selection and the beginning of the flu season (week 40 for the Northern Hemisphere) can seriously hamper vaccine efficacy. For this reason, in most years, the flu vaccine is 50 to 70% effective (Center for Disease Control and Prevention, 2014a). In most years, the flu vaccine is 50 to 70% effective (Center for Disease Control and Prevention, 2014a) This is especially a concern for influenza A viruses which evolve more rapidly than influenza B viruses (Nobusawa and Sato, 2006)
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