A quantitative genotype algorithm reflecting H5N1 Avian influenza niches
Identifieur interne : 001413 ( Main/Exploration ); précédent : 001412; suivant : 001414A quantitative genotype algorithm reflecting H5N1 Avian influenza niches
Auteurs : Xiu-Feng Wan [États-Unis] ; Guorong Chen [États-Unis] ; Feng Luo [États-Unis] ; Michael Emch [États-Unis] ; Ruben Donis [États-Unis]Source :
- Bioinformatics [ 1367-4803 ] ; 2007.
Abstract
Motivation: Computational genotyping analyses are critical for characterizing molecular evolutionary footprints, thus providing important information for designing the strategies of influenza prevention and control. Most of the current methods that are available are based on multiple sequence alignment and phylogenetic tree construction, which are time consuming and limited by the number of taxa. Arbitrarily defining genotypes further complicates the interpretation of genotyping results. Methods: In this study, we describe a quantitative influenza genotyping algorithm based on the theory of quasispecies. First, the complete composition vector (CCV) was utilized to calculate the pairwise evolutionary distance between genotypes. Next, Hierarchical Bayesian Modeling using the Gibbs Sampling algorithm was applied to identify the segment genotype threshold, which is used to identify influenza segment genotype through a modularity calculation. The viral genotype was defined by combining eight segment genotypes based on the genetic reassortment feature of influenza A viruses. Results: We applied this method for H5N1 avian influenza viruses and identified 107 niches among 283 viruses with a complete genome set. The diversity of viral genotypes, and their correlation with geographic locations suggests that these viruses form local niches after being introduced to a new ecological environment through poultry trade or bird migration. This novel method allows us to define genotypes in a robust, quantitative as well as hierarchical manner. Contact: wanhenry@yahoo.com or fvq7@cdc.gov Supplementary information: Supplementary data are available at Bioinformatics online.
Url:
DOI: 10.1093/bioinformatics/btm354
Affiliations:
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Control, Atlanta, GA 30333</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Luo, Feng" sort="Luo, Feng" uniqKey="Luo F" first="Feng" last="Luo">Feng Luo</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Microbiology, Miami University, Oxford, OH 45056, School of Computing, Clemson University, Clemson, SC 29634, Department of Geography, University of North Carolina, Chapel Hill, NC 27516 and Influenza Division, Centers for Disease Prevention and Control, Atlanta, GA 30333</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Emch, Michael" sort="Emch, Michael" uniqKey="Emch M" first="Michael" last="Emch">Michael Emch</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Microbiology, Miami University, Oxford, OH 45056, School of Computing, Clemson University, Clemson, SC 29634, Department of Geography, University of North Carolina, Chapel Hill, NC 27516 and Influenza Division, Centers for Disease Prevention and Control, Atlanta, GA 30333</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Donis, Ruben" sort="Donis, Ruben" uniqKey="Donis R" first="Ruben" last="Donis">Ruben Donis</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Microbiology, Miami University, Oxford, OH 45056, School of Computing, Clemson University, Clemson, SC 29634, Department of Geography, University of North Carolina, Chapel Hill, NC 27516 and Influenza Division, Centers for Disease Prevention and Control, Atlanta, GA 30333</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Bioinformatics</title><idno type="ISSN">1367-4803</idno><idno type="eISSN">1460-2059</idno><imprint><publisher>Oxford University Press</publisher><date when="2007-09-15">2007</date><date type="e-published" when="2007-07-10">2007</date><biblScope unit="vol">23</biblScope><biblScope unit="issue">18</biblScope><biblScope unit="page" from="2368">2368</biblScope><biblScope unit="page" to="2375">2375</biblScope></imprint><idno type="ISSN">1367-4803</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1367-4803</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">Motivation: Computational genotyping analyses are critical for characterizing molecular evolutionary footprints, thus providing important information for designing the strategies of influenza prevention and control. Most of the current methods that are available are based on multiple sequence alignment and phylogenetic tree construction, which are time consuming and limited by the number of taxa. Arbitrarily defining genotypes further complicates the interpretation of genotyping results. Methods: In this study, we describe a quantitative influenza genotyping algorithm based on the theory of quasispecies. First, the complete composition vector (CCV) was utilized to calculate the pairwise evolutionary distance between genotypes. Next, Hierarchical Bayesian Modeling using the Gibbs Sampling algorithm was applied to identify the segment genotype threshold, which is used to identify influenza segment genotype through a modularity calculation. The viral genotype was defined by combining eight segment genotypes based on the genetic reassortment feature of influenza A viruses. Results: We applied this method for H5N1 avian influenza viruses and identified 107 niches among 283 viruses with a complete genome set. The diversity of viral genotypes, and their correlation with geographic locations suggests that these viruses form local niches after being introduced to a new ecological environment through poultry trade or bird migration. This novel method allows us to define genotypes in a robust, quantitative as well as hierarchical manner. Contact: wanhenry@yahoo.com or fvq7@cdc.gov Supplementary information: Supplementary data are available at Bioinformatics online.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Géorgie (États-Unis)</li></region></list><tree><country name="États-Unis"><region name="Géorgie (États-Unis)"><name sortKey="Wan, Xiu Feng" sort="Wan, Xiu Feng" uniqKey="Wan X" first="Xiu-Feng" last="Wan">Xiu-Feng Wan</name></region><name sortKey="Chen, Guorong" sort="Chen, Guorong" uniqKey="Chen G" first="Guorong" last="Chen">Guorong Chen</name><name sortKey="Donis, Ruben" sort="Donis, Ruben" uniqKey="Donis R" first="Ruben" last="Donis">Ruben Donis</name><name sortKey="Emch, Michael" sort="Emch, Michael" uniqKey="Emch M" first="Michael" last="Emch">Michael Emch</name><name sortKey="Luo, Feng" sort="Luo, Feng" uniqKey="Luo F" first="Feng" last="Luo">Feng Luo</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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