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Article: FluClass: A novel algorithm and approach to score and visualize the phylogeny of the influenza virus using mass spectrometry

TitleFluClass: A novel algorithm and approach to score and visualize the phylogeny of the influenza virus using mass spectrometry
Authors
KeywordsPhylogenetics
Algorithm
Influenza virus
Mass mapping
Mass spectrometry
Issue Date2015
Citation
Analytica Chimica Acta, 2015, v. 895, p. 54-61 How to Cite?
Abstract© 2015 Elsevier B.V. A novel computer algorithm FluClass has been developed to facilitate the phylogenetic classification of influenza virus using mass spectral data. FluClass accepts a DNA or protein-based phylogenetic tree as input and generates theoretical peptide mass lists for each node. An experimental mass spectrum from an influenza virus protein digest is then placed onto the phylogenetic tree using a novel random resampling function (Z-score) that allows the scoring of spectrum against both internal and leaf nodes. Testing of the algorithm using hemagglutinin protein sequences from human-host influenza viruses showed that the Z-score performs comparably to the Profound scoring method for the scoring of leaf nodes and is substantially better at scoring internal nodes. Scoring of internal nodes allows colorizations of nodes of the phylogenetic tree enabling the classification of the query spectrum to be rapidly visualized. Finally we demonstrate the utility of FluClass on experimental spectra from six strains. Given that mass spectrometry data can be generated rapidly for influenza virus proteins, FluClass provides a fast and direct method for phylogenetic analysis of influenza proteins.
Persistent Identifierhttp://hdl.handle.net/10722/251127
ISSN
2021 Impact Factor: 6.911
2020 SCImago Journal Rankings: 1.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, Shiyong-
dc.contributor.authorDownard, Kevin M.-
dc.contributor.authorWong, Jason W H-
dc.date.accessioned2018-02-01T01:54:41Z-
dc.date.available2018-02-01T01:54:41Z-
dc.date.issued2015-
dc.identifier.citationAnalytica Chimica Acta, 2015, v. 895, p. 54-61-
dc.identifier.issn0003-2670-
dc.identifier.urihttp://hdl.handle.net/10722/251127-
dc.description.abstract© 2015 Elsevier B.V. A novel computer algorithm FluClass has been developed to facilitate the phylogenetic classification of influenza virus using mass spectral data. FluClass accepts a DNA or protein-based phylogenetic tree as input and generates theoretical peptide mass lists for each node. An experimental mass spectrum from an influenza virus protein digest is then placed onto the phylogenetic tree using a novel random resampling function (Z-score) that allows the scoring of spectrum against both internal and leaf nodes. Testing of the algorithm using hemagglutinin protein sequences from human-host influenza viruses showed that the Z-score performs comparably to the Profound scoring method for the scoring of leaf nodes and is substantially better at scoring internal nodes. Scoring of internal nodes allows colorizations of nodes of the phylogenetic tree enabling the classification of the query spectrum to be rapidly visualized. Finally we demonstrate the utility of FluClass on experimental spectra from six strains. Given that mass spectrometry data can be generated rapidly for influenza virus proteins, FluClass provides a fast and direct method for phylogenetic analysis of influenza proteins.-
dc.languageeng-
dc.relation.ispartofAnalytica Chimica Acta-
dc.subjectPhylogenetics-
dc.subjectAlgorithm-
dc.subjectInfluenza virus-
dc.subjectMass mapping-
dc.subjectMass spectrometry-
dc.titleFluClass: A novel algorithm and approach to score and visualize the phylogeny of the influenza virus using mass spectrometry-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.aca.2015.09.004-
dc.identifier.pmid26454459-
dc.identifier.scopuseid_2-s2.0-84943448088-
dc.identifier.volume895-
dc.identifier.spage54-
dc.identifier.epage61-
dc.identifier.eissn1873-4324-
dc.identifier.isiWOS:000362945600005-
dc.identifier.issnl0003-2670-

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