Computational Methods for Predicting Effectors in Rust Pathogens.
Identifieur interne : 000070 ( Main/Exploration ); précédent : 000069; suivant : 000071Computational Methods for Predicting Effectors in Rust Pathogens.
Auteurs : Jana Sperschneider [Australie] ; Peter N. Dodds [Australie] ; Jennifer M. Taylor [Australie] ; Sébastien Duplessis [France]Source :
- Methods in molecular biology (Clifton, N.J.) [ 1940-6029 ] ; 2017.
Descripteurs français
- KwdFr :
- Basidiomycota (génétique), Basidiomycota (physiologie), Biologie informatique (méthodes), Famille multigénique (MeSH), Génome fongique (MeSH), Génomique (méthodes), Interactions hôte-pathogène (MeSH), Maladies des plantes (microbiologie), Plantes (microbiologie), Protéines fongiques (analyse), Protéines fongiques (génétique), Protéines fongiques (métabolisme).
- MESH :
- analyse : Protéines fongiques.
- génétique : Basidiomycota, Protéines fongiques.
- microbiologie : Maladies des plantes, Plantes.
- métabolisme : Protéines fongiques.
- méthodes : Biologie informatique, Génomique.
- physiologie : Basidiomycota.
- Famille multigénique, Génome fongique, Interactions hôte-pathogène.
English descriptors
- KwdEn :
- Basidiomycota (genetics), Basidiomycota (physiology), Computational Biology (methods), Fungal Proteins (analysis), Fungal Proteins (genetics), Fungal Proteins (metabolism), Genome, Fungal (MeSH), Genomics (methods), Host-Pathogen Interactions (MeSH), Multigene Family (MeSH), Plant Diseases (microbiology), Plants (microbiology).
- MESH :
- chemical , analysis : Fungal Proteins.
- genetics : Basidiomycota, Fungal Proteins.
- chemical , metabolism : Fungal Proteins.
- methods : Computational Biology, Genomics.
- microbiology : Plant Diseases, Plants.
- physiology : Basidiomycota.
- Genome, Fungal, Host-Pathogen Interactions, Multigene Family.
Abstract
Lower costs and improved sequencing technologies have led to a large number of high-quality rust pathogen genomes and deeper characterization of gene expression profiles during early and late infection stages. However, the set of secreted proteins expressed during infection is too large for experimental investigations and contains not only effectors but also proteins that play a role in niche colonization or in fighting off competing microbes. Therefore, accurate computational prediction is essential for identifying high-priority rust effector candidates from secretomes.
DOI: 10.1007/978-1-4939-7249-4_7
PubMed: 28856642
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Taylor</name><affiliation wicri:level="1"><nlm:affiliation>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT</wicri:regionArea><wicri:noRegion>ACT</wicri:noRegion></affiliation></author><author><name sortKey="Duplessis, Sebastien" sort="Duplessis, Sebastien" uniqKey="Duplessis S" first="Sébastien" last="Duplessis">Sébastien Duplessis</name><affiliation wicri:level="3"><nlm:affiliation>INRA, Unité Mixte de Recherche INRA/Université de Lorraine 1136 Interactions Arbres-Microorganismes, INRA Centre Grand Est - Nancy, Champenoux, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INRA, Unité Mixte de Recherche INRA/Université de Lorraine 1136 Interactions Arbres-Microorganismes, INRA Centre Grand Est - Nancy, Champenoux</wicri:regionArea><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Champenoux</settlement></placeName></affiliation></author></analytic><series><title level="j">Methods in molecular biology (Clifton, N.J.)</title><idno type="eISSN">1940-6029</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Basidiomycota (genetics)</term><term>Basidiomycota (physiology)</term><term>Computational Biology (methods)</term><term>Fungal Proteins (analysis)</term><term>Fungal Proteins (genetics)</term><term>Fungal Proteins (metabolism)</term><term>Genome, Fungal (MeSH)</term><term>Genomics (methods)</term><term>Host-Pathogen Interactions (MeSH)</term><term>Multigene Family (MeSH)</term><term>Plant Diseases (microbiology)</term><term>Plants (microbiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Basidiomycota (génétique)</term><term>Basidiomycota (physiologie)</term><term>Biologie informatique (méthodes)</term><term>Famille multigénique (MeSH)</term><term>Génome fongique (MeSH)</term><term>Génomique (méthodes)</term><term>Interactions hôte-pathogène (MeSH)</term><term>Maladies des plantes (microbiologie)</term><term>Plantes (microbiologie)</term><term>Protéines fongiques (analyse)</term><term>Protéines fongiques (génétique)</term><term>Protéines fongiques (métabolisme)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Fungal Proteins</term></keywords><keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Protéines fongiques</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Basidiomycota</term><term>Fungal Proteins</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Basidiomycota</term><term>Protéines fongiques</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Fungal Proteins</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Genomics</term></keywords><keywords scheme="MESH" qualifier="microbiologie" xml:lang="fr"><term>Maladies des plantes</term><term>Plantes</term></keywords><keywords scheme="MESH" qualifier="microbiology" xml:lang="en"><term>Plant Diseases</term><term>Plants</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Protéines fongiques</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Biologie informatique</term><term>Génomique</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Basidiomycota</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Basidiomycota</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Genome, Fungal</term><term>Host-Pathogen Interactions</term><term>Multigene Family</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Famille multigénique</term><term>Génome fongique</term><term>Interactions hôte-pathogène</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Lower costs and improved sequencing technologies have led to a large number of high-quality rust pathogen genomes and deeper characterization of gene expression profiles during early and late infection stages. However, the set of secreted proteins expressed during infection is too large for experimental investigations and contains not only effectors but also proteins that play a role in niche colonization or in fighting off competing microbes. Therefore, accurate computational prediction is essential for identifying high-priority rust effector candidates from secretomes.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28856642</PMID><DateCompleted><Year>2018</Year><Month>05</Month><Day>07</Day></DateCompleted><DateRevised><Year>2018</Year><Month>08</Month><Day>09</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1940-6029</ISSN><JournalIssue CitedMedium="Internet"><Volume>1659</Volume><PubDate><Year>2017</Year></PubDate></JournalIssue><Title>Methods in molecular biology (Clifton, N.J.)</Title><ISOAbbreviation>Methods Mol Biol</ISOAbbreviation></Journal><ArticleTitle>Computational Methods for Predicting Effectors in Rust Pathogens.</ArticleTitle><Pagination><MedlinePgn>73-83</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/978-1-4939-7249-4_7</ELocationID><Abstract><AbstractText>Lower costs and improved sequencing technologies have led to a large number of high-quality rust pathogen genomes and deeper characterization of gene expression profiles during early and late infection stages. However, the set of secreted proteins expressed during infection is too large for experimental investigations and contains not only effectors but also proteins that play a role in niche colonization or in fighting off competing microbes. Therefore, accurate computational prediction is essential for identifying high-priority rust effector candidates from secretomes.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sperschneider</LastName><ForeName>Jana</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Centre for Environmental and Life Sciences, CSIRO Agriculture and Food, Underwood Avenue, Floreat, WA, Australia. jana.sperschneider@csiro.au.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dodds</LastName><ForeName>Peter N</ForeName><Initials>PN</Initials><AffiliationInfo><Affiliation>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Taylor</LastName><ForeName>Jennifer M</ForeName><Initials>JM</Initials><AffiliationInfo><Affiliation>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Duplessis</LastName><ForeName>Sébastien</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>INRA, Unité Mixte de Recherche INRA/Université de Lorraine 1136 Interactions Arbres-Microorganismes, INRA Centre Grand Est - Nancy, Champenoux, France.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Methods Mol Biol</MedlineTA><NlmUniqueID>9214969</NlmUniqueID><ISSNLinking>1064-3745</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005656">Fungal Proteins</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001487" MajorTopicYN="N">Basidiomycota</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005656" MajorTopicYN="N">Fungal Proteins</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016681" MajorTopicYN="N">Genome, Fungal</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D023281" MajorTopicYN="N">Genomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D054884" MajorTopicYN="N">Host-Pathogen Interactions</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005810" MajorTopicYN="N">Multigene Family</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010935" MajorTopicYN="N">Plant Diseases</DescriptorName><QualifierName UI="Q000382" MajorTopicYN="Y">microbiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010944" MajorTopicYN="N">Plants</DescriptorName><QualifierName UI="Q000382" MajorTopicYN="N">microbiology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Diversifying selection</Keyword><Keyword MajorTopicYN="Y">Effector prediction</Keyword><Keyword MajorTopicYN="Y">Effectors</Keyword><Keyword MajorTopicYN="Y">Rust</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>9</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>9</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>5</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28856642</ArticleId><ArticleId IdType="doi">10.1007/978-1-4939-7249-4_7</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Champenoux</li></settlement></list><tree><country name="Australie"><noRegion><name sortKey="Sperschneider, Jana" sort="Sperschneider, Jana" uniqKey="Sperschneider J" first="Jana" last="Sperschneider">Jana Sperschneider</name></noRegion><name sortKey="Dodds, Peter N" sort="Dodds, Peter N" uniqKey="Dodds P" first="Peter N" last="Dodds">Peter N. Dodds</name><name sortKey="Taylor, Jennifer M" sort="Taylor, Jennifer M" uniqKey="Taylor J" first="Jennifer M" last="Taylor">Jennifer M. Taylor</name></country><country name="France"><region name="Grand Est"><name sortKey="Duplessis, Sebastien" sort="Duplessis, Sebastien" uniqKey="Duplessis S" first="Sébastien" last="Duplessis">Sébastien Duplessis</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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