A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.
Identifieur interne : 000A36 ( PubMed/Checkpoint ); précédent : 000A35; suivant : 000A37A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.
Auteurs : Erki Aun [Estonie] ; Age Brauer [Estonie] ; Veljo Kisand [Estonie] ; Tanel Tenson [Estonie] ; Maido Remm [Estonie]Source :
- PLoS computational biology [ 1553-7358 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ADN bactérien, Bactéries, Génome bactérien, Marqueurs génétiques.
- métabolisme : Bactéries.
- physiologie : ADN bactérien, Génome bactérien.
- Algorithmes, Alignement de séquences, Analyse de séquence d'ADN, Génomique, Logiciel, Phénotype.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : DNA, Bacterial, Genetic Markers.
- genetics : Bacteria, Genome, Bacterial.
- metabolism : Bacteria.
- methods : Genomics.
- chemical , physiology : DNA, Bacterial, Genome, Bacterial.
- Algorithms, Phenotype, Sequence Alignment, Sequence Analysis, DNA, Software.
Abstract
We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).
DOI: 10.1371/journal.pcbi.1006434
PubMed: 30346947
Affiliations:
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pubmed:30346947Le document en format XML
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Biology, University of Tartu, Tartu</wicri:regionArea><wicri:noRegion>Tartu</wicri:noRegion></affiliation></author><author><name sortKey="Brauer, Age" sort="Brauer, Age" uniqKey="Brauer A" first="Age" last="Brauer">Age Brauer</name><affiliation wicri:level="1"><nlm:affiliation>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.</nlm:affiliation><country xml:lang="fr">Estonie</country><wicri:regionArea>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu</wicri:regionArea><wicri:noRegion>Tartu</wicri:noRegion></affiliation></author><author><name sortKey="Kisand, Veljo" sort="Kisand, Veljo" uniqKey="Kisand V" first="Veljo" last="Kisand">Veljo Kisand</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Technology, University of Tartu, Tartu, Estonia.</nlm:affiliation><country xml:lang="fr">Estonie</country><wicri:regionArea>Institute of Technology, University of Tartu, Tartu</wicri:regionArea><wicri:noRegion>Tartu</wicri:noRegion></affiliation></author><author><name sortKey="Tenson, Tanel" sort="Tenson, Tanel" uniqKey="Tenson T" first="Tanel" last="Tenson">Tanel Tenson</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Technology, University of Tartu, Tartu, Estonia.</nlm:affiliation><country xml:lang="fr">Estonie</country><wicri:regionArea>Institute of Technology, University of Tartu, Tartu</wicri:regionArea><wicri:noRegion>Tartu</wicri:noRegion></affiliation></author><author><name sortKey="Remm, Maido" sort="Remm, Maido" uniqKey="Remm M" first="Maido" last="Remm">Maido Remm</name><affiliation wicri:level="1"><nlm:affiliation>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.</nlm:affiliation><country xml:lang="fr">Estonie</country><wicri:regionArea>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu</wicri:regionArea><wicri:noRegion>Tartu</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS computational biology</title><idno type="eISSN">1553-7358</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Bacteria (genetics)</term><term>Bacteria (metabolism)</term><term>DNA, Bacterial (genetics)</term><term>DNA, Bacterial (physiology)</term><term>Genetic Markers (genetics)</term><term>Genome, Bacterial (genetics)</term><term>Genome, Bacterial (physiology)</term><term>Genomics (methods)</term><term>Phenotype</term><term>Sequence Alignment</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ADN bactérien (génétique)</term><term>ADN bactérien (physiologie)</term><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence d'ADN</term><term>Bactéries (génétique)</term><term>Bactéries (métabolisme)</term><term>Génome bactérien (génétique)</term><term>Génome bactérien (physiologie)</term><term>Génomique ()</term><term>Logiciel</term><term>Marqueurs génétiques (génétique)</term><term>Phénotype</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>DNA, Bacterial</term><term>Genetic Markers</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Bacteria</term><term>Genome, Bacterial</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ADN bactérien</term><term>Bactéries</term><term>Génome bactérien</term><term>Marqueurs génétiques</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Bacteria</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Bactéries</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>ADN bactérien</term><term>Génome bactérien</term></keywords><keywords scheme="MESH" type="chemical" qualifier="physiology" xml:lang="en"><term>DNA, Bacterial</term><term>Genome, Bacterial</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Phenotype</term><term>Sequence Alignment</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence d'ADN</term><term>Génomique</term><term>Logiciel</term><term>Phénotype</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30346947</PMID><DateCompleted><Year>2019</Year><Month>01</Month><Day>28</Day></DateCompleted><DateRevised><Year>2019</Year><Month>01</Month><Day>28</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>10</Issue><PubDate><Year>2018</Year><Month>10</Month></PubDate></JournalIssue><Title>PLoS computational biology</Title><ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.</ArticleTitle><Pagination><MedlinePgn>e1006434</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1006434</ELocationID><Abstract><AbstractText>We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Aun</LastName><ForeName>Erki</ForeName><Initials>E</Initials><Identifier Source="ORCID">0000-0001-7446-3524</Identifier><AffiliationInfo><Affiliation>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Brauer</LastName><ForeName>Age</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kisand</LastName><ForeName>Veljo</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Institute of Technology, University of Tartu, Tartu, Estonia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tenson</LastName><ForeName>Tanel</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Institute of Technology, University of Tartu, Tartu, Estonia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Remm</LastName><ForeName>Maido</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0003-3966-8422</Identifier><AffiliationInfo><Affiliation>Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>10</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Comput Biol</MedlineTA><NlmUniqueID>101238922</NlmUniqueID><ISSNLinking>1553-734X</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D004269">DNA, Bacterial</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005819">Genetic Markers</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001419" MajorTopicYN="N">Bacteria</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004269" MajorTopicYN="N">DNA, Bacterial</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005819" MajorTopicYN="N">Genetic Markers</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016680" MajorTopicYN="N">Genome, Bacterial</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D023281" MajorTopicYN="N">Genomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010641" MajorTopicYN="N">Phenotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016415" MajorTopicYN="N">Sequence Alignment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="N">Software</DescriptorName></MeshHeading></MeshHeadingList><CoiStatement>The authors have declared that no competing interests exist.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>04</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>08</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>11</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>10</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>1</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>10</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30346947</ArticleId><ArticleId IdType="doi">10.1371/journal.pcbi.1006434</ArticleId><ArticleId IdType="pii">PCOMPBIOL-D-18-00544</ArticleId><ArticleId IdType="pmc">PMC6211763</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Microb Drug Resist. 1998 Winter;4(4):257-61</Citation><ArticleIdList><ArticleId IdType="pubmed">9988043</ArticleId></ArticleIdList></Reference><Reference><Citation>J Mol Biol. 1994 Mar 4;236(4):1067-78</Citation><ArticleIdList><ArticleId IdType="pubmed">8120887</ArticleId></ArticleIdList></Reference><Reference><Citation>Nucleic Acids Res. 2017 Oct 13;45(18):e159</Citation><ArticleIdList><ArticleId IdType="pubmed">29048594</ArticleId></ArticleIdList></Reference><Reference><Citation>Future Microbiol. 2016;11(3):455-66</Citation><ArticleIdList><ArticleId IdType="pubmed">26974504</ArticleId></ArticleIdList></Reference><Reference><Citation>J Bacteriol. 2010 Jun;192(12):3144-58</Citation><ArticleIdList><ArticleId 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