Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses
Identifieur interne : 001938 ( Main/Exploration ); précédent : 001937; suivant : 001939Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses
Auteurs : Ricardo Aguas ; Neil M. FergusonSource :
- PLoS Computational Biology [ 1553-734X ] ; 2013.
Descripteurs français
- KwdFr :
- Algorithmes, Analyse en composantes principales, Animaux, Biologie informatique (), Chiroptera (virologie), Gènes viraux (génétique), Humains, Intelligence artificielle, Interactions hôte-pathogène (génétique), Maladies virales (virologie), Phénotype, Protéines virales (), Protéines virales (génétique), Protéines virales (métabolisme), Récepteurs de surface cellulaire (), Récepteurs de surface cellulaire (génétique), Récepteurs de surface cellulaire (métabolisme), Sites de fixation, Virus à ARN (génétique), Zoonoses, Évolution moléculaire.
- MESH :
- génétique : Gènes viraux, Interactions hôte-pathogène, Protéines virales, Récepteurs de surface cellulaire, Virus à ARN.
- métabolisme : Protéines virales, Récepteurs de surface cellulaire.
- virologie : Chiroptera, Maladies virales.
- Algorithmes, Analyse en composantes principales, Animaux, Biologie informatique, Humains, Intelligence artificielle, Phénotype, Protéines virales, Récepteurs de surface cellulaire, Sites de fixation, Zoonoses, Évolution moléculaire.
English descriptors
- KwdEn :
- Algorithms, Animals, Artificial Intelligence, Binding Sites, Chiroptera (virology), Computational Biology (methods), Evolution, Molecular, Genes, Viral (genetics), Host-Pathogen Interactions (genetics), Humans, Phenotype, Principal Component Analysis, RNA Viruses (genetics), Receptors, Cell Surface (chemistry), Receptors, Cell Surface (genetics), Receptors, Cell Surface (metabolism), Viral Proteins (chemistry), Viral Proteins (genetics), Viral Proteins (metabolism), Virus Diseases (virology), Zoonoses.
- MESH :
- chemical , chemistry : Receptors, Cell Surface, Viral Proteins.
- genetics : Genes, Viral, Host-Pathogen Interactions, RNA Viruses, Receptors, Cell Surface, Viral Proteins.
- chemical , metabolism : Receptors, Cell Surface, Viral Proteins.
- methods : Computational Biology.
- virology : Chiroptera, Virus Diseases.
- Algorithms, Animals, Artificial Intelligence, Binding Sites, Evolution, Molecular, Humans, Phenotype, Principal Component Analysis, Zoonoses.
Abstract
Despite environmental, social and ecological dependencies, emergence of zoonotic viruses in human populations is clearly also affected by genetic factors which determine cross-species transmission potential. RNA viruses pose an interesting case study given their mutation rates are orders of magnitude higher than any other pathogen – as reflected by the recent emergence of SARS and Influenza for example. Here, we show how feature selection techniques can be used to reliably classify viral sequences by host species, and to identify the crucial minority of host-specific sites in pathogen genomic data. The variability in alleles at those sites can be translated into prediction probabilities that a particular pathogen isolate is adapted to a given host. We illustrate the power of these methods by: 1) identifying the sites explaining SARS coronavirus differences between human, bat and palm civet samples; 2) showing how cross species jumps of rabies virus among bat populations can be readily identified; and 3)
Url:
DOI: 10.1371/journal.pcbi.1003254
PubMed: 24130470
PubMed Central: 3794897
Affiliations:
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Le document en format XML
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Ferguson</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS Computational Biology</title><idno type="ISSN">1553-734X</idno><idno type="eISSN">1553-7358</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Artificial Intelligence</term><term>Binding Sites</term><term>Chiroptera (virology)</term><term>Computational Biology (methods)</term><term>Evolution, Molecular</term><term>Genes, Viral (genetics)</term><term>Host-Pathogen Interactions (genetics)</term><term>Humans</term><term>Phenotype</term><term>Principal Component Analysis</term><term>RNA Viruses (genetics)</term><term>Receptors, Cell Surface (chemistry)</term><term>Receptors, Cell Surface (genetics)</term><term>Receptors, Cell Surface (metabolism)</term><term>Viral Proteins (chemistry)</term><term>Viral Proteins (genetics)</term><term>Viral Proteins (metabolism)</term><term>Virus Diseases (virology)</term><term>Zoonoses</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse en composantes principales</term><term>Animaux</term><term>Biologie informatique ()</term><term>Chiroptera (virologie)</term><term>Gènes viraux (génétique)</term><term>Humains</term><term>Intelligence artificielle</term><term>Interactions hôte-pathogène (génétique)</term><term>Maladies virales (virologie)</term><term>Phénotype</term><term>Protéines virales ()</term><term>Protéines virales (génétique)</term><term>Protéines virales (métabolisme)</term><term>Récepteurs de surface cellulaire ()</term><term>Récepteurs de surface cellulaire (génétique)</term><term>Récepteurs de surface cellulaire (métabolisme)</term><term>Sites de fixation</term><term>Virus à ARN (génétique)</term><term>Zoonoses</term><term>Évolution moléculaire</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Receptors, Cell Surface</term><term>Viral Proteins</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Genes, Viral</term><term>Host-Pathogen Interactions</term><term>RNA Viruses</term><term>Receptors, Cell Surface</term><term>Viral Proteins</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Gènes viraux</term><term>Interactions hôte-pathogène</term><term>Protéines virales</term><term>Récepteurs de surface cellulaire</term><term>Virus à ARN</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Receptors, Cell Surface</term><term>Viral Proteins</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Protéines virales</term><term>Récepteurs de surface cellulaire</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Chiroptera</term><term>Maladies virales</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Chiroptera</term><term>Virus Diseases</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Artificial Intelligence</term><term>Binding Sites</term><term>Evolution, Molecular</term><term>Humans</term><term>Phenotype</term><term>Principal Component Analysis</term><term>Zoonoses</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse en composantes principales</term><term>Animaux</term><term>Biologie informatique</term><term>Humains</term><term>Intelligence artificielle</term><term>Phénotype</term><term>Protéines virales</term><term>Récepteurs de surface cellulaire</term><term>Sites de fixation</term><term>Zoonoses</term><term>Évolution moléculaire</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Despite environmental, social and ecological dependencies, emergence of zoonotic viruses in human populations is clearly also affected by genetic factors which determine cross-species transmission potential. RNA viruses pose an interesting case study given their mutation rates are orders of magnitude higher than any other pathogen – as reflected by the recent emergence of SARS and Influenza for example. Here, we show how feature selection techniques can be used to reliably classify viral sequences by host species, and to identify the crucial minority of host-specific sites in pathogen genomic data. The variability in alleles at those sites can be translated into prediction probabilities that a particular pathogen isolate is adapted to a given host. We illustrate the power of these methods by: 1) identifying the sites explaining SARS coronavirus differences between human, bat and palm civet samples; 2) showing how cross species jumps of rabies virus among bat populations can be readily identified; and 3) <italic>de novo</italic> identification of likely functional influenza host discriminant markers.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Cleaveland, S" uniqKey="Cleaveland S">S Cleaveland</name></author><author><name sortKey="Laurenson, Mk" uniqKey="Laurenson M">MK Laurenson</name></author><author><name sortKey="Taylor, Lh" uniqKey="Taylor L">LH Taylor</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jones, Ke" uniqKey="Jones K">KE Jones</name></author><author><name sortKey="Patel, Ng" uniqKey="Patel N">NG Patel</name></author><author><name sortKey="Levy, Ma" uniqKey="Levy M">MA Levy</name></author><author><name sortKey="Storeygard, 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