Cargando…

Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information

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 influe...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Jialiang, Zhang, Tong, Wan, Xiu-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154722/
https://www.ncbi.nlm.nih.gov/pubmed/25188236
http://dx.doi.org/10.1371/journal.pone.0106660
_version_ 1782333462562209792
author Yang, Jialiang
Zhang, Tong
Wan, Xiu-Feng
author_facet Yang, Jialiang
Zhang, Tong
Wan, Xiu-Feng
author_sort Yang, Jialiang
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4154722
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-41547222014-09-08 Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information Yang, Jialiang Zhang, Tong Wan, Xiu-Feng PLoS One Research Article 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. Public Library of Science 2014-09-04 /pmc/articles/PMC4154722/ /pubmed/25188236 http://dx.doi.org/10.1371/journal.pone.0106660 Text en © 2014 Yang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Yang, Jialiang
Zhang, Tong
Wan, Xiu-Feng
Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information
title Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information
title_full Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information
title_fullStr Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information
title_full_unstemmed Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information
title_short Sequence-Based Antigenic Change Prediction by a Sparse Learning Method Incorporating Co-Evolutionary Information
title_sort sequence-based antigenic change prediction by a sparse learning method incorporating co-evolutionary information
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154722/
https://www.ncbi.nlm.nih.gov/pubmed/25188236
http://dx.doi.org/10.1371/journal.pone.0106660
work_keys_str_mv AT yangjialiang sequencebasedantigenicchangepredictionbyasparselearningmethodincorporatingcoevolutionaryinformation
AT zhangtong sequencebasedantigenicchangepredictionbyasparselearningmethodincorporatingcoevolutionaryinformation
AT wanxiufeng sequencebasedantigenicchangepredictionbyasparselearningmethodincorporatingcoevolutionaryinformation