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FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data
BACKGROUND: High resolution mass spectrometry has been employed to rapidly and accurately type and subtype influenza viruses. The detection of signature peptides with unique theoretical masses enables the unequivocal assignment of the type and subtype of a given strain. This analysis has, to date, r...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3098065/ https://www.ncbi.nlm.nih.gov/pubmed/20482883 http://dx.doi.org/10.1186/1471-2105-11-266 |
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author | Wong, Jason WH Schwahn, Alexander B Downard, Kevin M |
author_facet | Wong, Jason WH Schwahn, Alexander B Downard, Kevin M |
author_sort | Wong, Jason WH |
collection | PubMed |
description | BACKGROUND: High resolution mass spectrometry has been employed to rapidly and accurately type and subtype influenza viruses. The detection of signature peptides with unique theoretical masses enables the unequivocal assignment of the type and subtype of a given strain. This analysis has, to date, required the manual inspection of mass spectra of whole virus and antigen digests. RESULTS: A computer algorithm, FluTyper, has been designed and implemented to achieve the automated analysis of MALDI mass spectra recorded for proteolytic digests of the whole influenza virus and antigens. FluTyper incorporates the use of established signature peptides and newly developed naïve Bayes classifiers for four common influenza antigens, hemagglutinin, neuraminidase, nucleoprotein, and matrix protein 1, to type and subtype the influenza virus based on their detection within proteolytic peptide mass maps. Theoretical and experimental testing of the classifiers demonstrates their applicability at protein coverage rates normally achievable in mass mapping experiments. The application of FluTyper to whole virus and antigen digests of a range of different strains of the influenza virus is demonstrated. CONCLUSIONS: FluTyper algorithm facilitates the rapid and automated typing and subtyping of the influenza virus from mass spectral data. The newly developed naïve Bayes classifiers increase the confidence of influenza virus subtyping, especially where signature peptides are not detected. FluTyper is expected to popularize the use of mass spectrometry to characterize influenza viruses. |
format | Text |
id | pubmed-3098065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30980652011-05-20 FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data Wong, Jason WH Schwahn, Alexander B Downard, Kevin M BMC Bioinformatics Methodology Article BACKGROUND: High resolution mass spectrometry has been employed to rapidly and accurately type and subtype influenza viruses. The detection of signature peptides with unique theoretical masses enables the unequivocal assignment of the type and subtype of a given strain. This analysis has, to date, required the manual inspection of mass spectra of whole virus and antigen digests. RESULTS: A computer algorithm, FluTyper, has been designed and implemented to achieve the automated analysis of MALDI mass spectra recorded for proteolytic digests of the whole influenza virus and antigens. FluTyper incorporates the use of established signature peptides and newly developed naïve Bayes classifiers for four common influenza antigens, hemagglutinin, neuraminidase, nucleoprotein, and matrix protein 1, to type and subtype the influenza virus based on their detection within proteolytic peptide mass maps. Theoretical and experimental testing of the classifiers demonstrates their applicability at protein coverage rates normally achievable in mass mapping experiments. The application of FluTyper to whole virus and antigen digests of a range of different strains of the influenza virus is demonstrated. CONCLUSIONS: FluTyper algorithm facilitates the rapid and automated typing and subtyping of the influenza virus from mass spectral data. The newly developed naïve Bayes classifiers increase the confidence of influenza virus subtyping, especially where signature peptides are not detected. FluTyper is expected to popularize the use of mass spectrometry to characterize influenza viruses. BioMed Central 2010-05-19 /pmc/articles/PMC3098065/ /pubmed/20482883 http://dx.doi.org/10.1186/1471-2105-11-266 Text en Copyright ©2010 Wong et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Wong, Jason WH Schwahn, Alexander B Downard, Kevin M FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
title | FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
title_full | FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
title_fullStr | FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
title_full_unstemmed | FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
title_short | FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
title_sort | flutyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3098065/ https://www.ncbi.nlm.nih.gov/pubmed/20482883 http://dx.doi.org/10.1186/1471-2105-11-266 |
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