Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary InformationReport as inadecuate




Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information - Download this document for free, or read online. Document in PDF available to download.

Rapid identification of influenza antigenic variants will be critical in selecting optimal vaccine candidates and thus a key to developing an effective vaccination program. Recent studies suggest that multiple simultaneous mutations at antigenic sites accumulatively enhance antigenic drift of influenza A viruses. However, pre-existing methods on antigenic variant identification are based on analyses from individual sites. Because the impacts of these co-evolved sites on influenza antigenicity may not be additive, it will be critical to quantify the impact of not only those single mutations but also multiple simultaneous mutations or co-evolved sites. Here, we developed and applied a computational method, AntigenCO, to identify and quantify both single and co-evolutionary sites driving the historical antigenic drifts. AntigenCO achieved an accuracy of up to 90.05% for antigenic variant prediction, significantly outperforming methods based on single sites. AntigenCO can be useful in antigenic variant identification in influenza surveillance.



Author: Jialiang Yang, Tong Zhang, Xiu-Feng Wan

Source: http://plos.srce.hr/



DOWNLOAD PDF




Related documents