Comparing genome versus proteome-based identification of clinical bacterial isolates.
Identifieur interne : 000E37 ( PubMed/Corpus ); précédent : 000E36; suivant : 000E38Comparing genome versus proteome-based identification of clinical bacterial isolates.
Auteurs : Valentina Galata ; Christina Backes ; Cédric Christian Laczny ; Georg Hemmrich-Stanisak ; Howard Li ; Laura Smoot ; Andreas Emanuel Posch ; Susanne Schmolke ; Markus Bischoff ; Lutz Von Müller ; Achim Plum ; Andre Franke ; Andreas KellerSource :
- Briefings in bioinformatics [ 1477-4054 ] ; 2018.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : Bacterial Proteins.
- chemical , metabolism : Bacterial Proteins.
- genetics : Gram-Negative Bacteria.
- isolation & purification : Gram-Negative Bacteria.
- metabolism : Gram-Negative Bacteria.
- methods : Whole Genome Sequencing.
- Genome, Bacterial, Humans, Proteome.
Abstract
Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.
DOI: 10.1093/bib/bbw122
PubMed: 28013236
Links to Exploration step
pubmed:28013236Le document en format XML
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for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Hemmrich Stanisak, Georg" sort="Hemmrich Stanisak, Georg" uniqKey="Hemmrich Stanisak G" first="Georg" last="Hemmrich-Stanisak">Georg Hemmrich-Stanisak</name><affiliation><nlm:affiliation>Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Howard" sort="Li, Howard" uniqKey="Li H" first="Howard" last="Li">Howard Li</name><affiliation><nlm:affiliation>Siemens Healthcare, R&D, 725 Potter Street, Berkeley, CA 94710, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Smoot, Laura" sort="Smoot, Laura" uniqKey="Smoot L" first="Laura" last="Smoot">Laura Smoot</name><affiliation><nlm:affiliation>Siemens Healthcare, R&D, 1584 Enterprise Blvd., West Sacramento, CA 95691, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Posch, Andreas Emanuel" sort="Posch, Andreas Emanuel" uniqKey="Posch A" first="Andreas Emanuel" last="Posch">Andreas Emanuel Posch</name><affiliation><nlm:affiliation>Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Schmolke, Susanne" sort="Schmolke, Susanne" uniqKey="Schmolke S" first="Susanne" last="Schmolke">Susanne Schmolke</name><affiliation><nlm:affiliation>Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Bischoff, Markus" sort="Bischoff, Markus" uniqKey="Bischoff M" first="Markus" last="Bischoff">Markus Bischoff</name><affiliation><nlm:affiliation>Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße, Campus Building 43, 66421 Homburg/Saar, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Von Muller, Lutz" sort="Von Muller, Lutz" uniqKey="Von Muller L" first="Lutz" last="Von Müller">Lutz Von Müller</name><affiliation><nlm:affiliation>Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße, Campus Building 43, 66421 Homburg/Saar, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Plum, Achim" sort="Plum, Achim" uniqKey="Plum A" first="Achim" last="Plum">Achim Plum</name><affiliation><nlm:affiliation>Curetis GmbH, Max-Eyth-Str. 42, 71088 Holzgeringen, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Franke, Andre" sort="Franke, Andre" uniqKey="Franke A" first="Andre" last="Franke">Andre Franke</name><affiliation><nlm:affiliation>Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Keller, Andreas" sort="Keller, Andreas" uniqKey="Keller A" first="Andreas" last="Keller">Andreas Keller</name><affiliation><nlm:affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:28013236</idno><idno type="pmid">28013236</idno><idno type="doi">10.1093/bib/bbw122</idno><idno type="wicri:Area/PubMed/Corpus">000E37</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000E37</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Comparing genome versus proteome-based identification of clinical bacterial isolates.</title><author><name sortKey="Galata, Valentina" sort="Galata, Valentina" uniqKey="Galata V" first="Valentina" last="Galata">Valentina Galata</name><affiliation><nlm:affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Backes, Christina" sort="Backes, Christina" uniqKey="Backes C" first="Christina" last="Backes">Christina Backes</name><affiliation><nlm:affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Laczny, Cedric Christian" sort="Laczny, Cedric Christian" uniqKey="Laczny C" first="Cédric Christian" last="Laczny">Cédric Christian Laczny</name><affiliation><nlm:affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Hemmrich Stanisak, Georg" sort="Hemmrich Stanisak, Georg" uniqKey="Hemmrich Stanisak G" first="Georg" last="Hemmrich-Stanisak">Georg Hemmrich-Stanisak</name><affiliation><nlm:affiliation>Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Howard" sort="Li, Howard" uniqKey="Li H" first="Howard" last="Li">Howard Li</name><affiliation><nlm:affiliation>Siemens Healthcare, R&D, 725 Potter Street, Berkeley, CA 94710, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Smoot, Laura" sort="Smoot, Laura" uniqKey="Smoot L" first="Laura" last="Smoot">Laura Smoot</name><affiliation><nlm:affiliation>Siemens Healthcare, R&D, 1584 Enterprise Blvd., West Sacramento, CA 95691, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Posch, Andreas Emanuel" sort="Posch, Andreas Emanuel" uniqKey="Posch A" first="Andreas Emanuel" last="Posch">Andreas Emanuel Posch</name><affiliation><nlm:affiliation>Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Schmolke, Susanne" sort="Schmolke, Susanne" uniqKey="Schmolke S" first="Susanne" last="Schmolke">Susanne Schmolke</name><affiliation><nlm:affiliation>Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Bischoff, Markus" sort="Bischoff, Markus" uniqKey="Bischoff M" first="Markus" last="Bischoff">Markus Bischoff</name><affiliation><nlm:affiliation>Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße, Campus Building 43, 66421 Homburg/Saar, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Von Muller, Lutz" sort="Von Muller, Lutz" uniqKey="Von Muller L" first="Lutz" last="Von Müller">Lutz Von Müller</name><affiliation><nlm:affiliation>Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße, Campus Building 43, 66421 Homburg/Saar, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Plum, Achim" sort="Plum, Achim" uniqKey="Plum A" first="Achim" last="Plum">Achim Plum</name><affiliation><nlm:affiliation>Curetis GmbH, Max-Eyth-Str. 42, 71088 Holzgeringen, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Franke, Andre" sort="Franke, Andre" uniqKey="Franke A" first="Andre" last="Franke">Andre Franke</name><affiliation><nlm:affiliation>Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Keller, Andreas" sort="Keller, Andreas" uniqKey="Keller A" first="Andreas" last="Keller">Andreas Keller</name><affiliation><nlm:affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Briefings in bioinformatics</title><idno type="eISSN">1477-4054</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bacterial Proteins (genetics)</term><term>Bacterial Proteins (metabolism)</term><term>Genome, Bacterial</term><term>Gram-Negative Bacteria (genetics)</term><term>Gram-Negative Bacteria (isolation & purification)</term><term>Gram-Negative Bacteria (metabolism)</term><term>Humans</term><term>Proteome</term><term>Whole Genome Sequencing (methods)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Bacterial Proteins</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Bacterial Proteins</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Gram-Negative Bacteria</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Gram-Negative Bacteria</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Gram-Negative Bacteria</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Whole Genome Sequencing</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Genome, Bacterial</term><term>Humans</term><term>Proteome</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28013236</PMID><DateCompleted><Year>2019</Year><Month>03</Month><Day>26</Day></DateCompleted><DateRevised><Year>2019</Year><Month>03</Month><Day>26</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1477-4054</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>3</Issue><PubDate><Year>2018</Year><Month>05</Month><Day>01</Day></PubDate></JournalIssue><Title>Briefings in bioinformatics</Title><ISOAbbreviation>Brief. Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Comparing genome versus proteome-based identification of clinical bacterial isolates.</ArticleTitle><Pagination><MedlinePgn>495-505</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bib/bbw122</ELocationID><Abstract><AbstractText>Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Galata</LastName><ForeName>Valentina</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Backes</LastName><ForeName>Christina</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Laczny</LastName><ForeName>Cédric Christian</ForeName><Initials>CC</Initials><AffiliationInfo><Affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hemmrich-Stanisak</LastName><ForeName>Georg</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Howard</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Siemens Healthcare, R&D, 725 Potter Street, Berkeley, CA 94710, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Smoot</LastName><ForeName>Laura</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Siemens Healthcare, R&D, 1584 Enterprise Blvd., West Sacramento, CA 95691, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Posch</LastName><ForeName>Andreas Emanuel</ForeName><Initials>AE</Initials><AffiliationInfo><Affiliation>Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schmolke</LastName><ForeName>Susanne</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Siemens Healthcare GmbH, Strategy and Innovation, Hartmannstr. 16, 91052 Erlangen, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bischoff</LastName><ForeName>Markus</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße, Campus Building 43, 66421 Homburg/Saar, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>von Müller</LastName><ForeName>Lutz</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße, Campus Building 43, 66421 Homburg/Saar, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Plum</LastName><ForeName>Achim</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Curetis GmbH, Max-Eyth-Str. 42, 71088 Holzgeringen, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Franke</LastName><ForeName>Andre</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Schittenhelmstr. 12, 24105 Kiel, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Keller</LastName><ForeName>Andreas</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Brief Bioinform</MedlineTA><NlmUniqueID>100912837</NlmUniqueID><ISSNLinking>1467-5463</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D001426">Bacterial Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D020543">Proteome</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001426" MajorTopicYN="N">Bacterial Proteins</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016680" MajorTopicYN="Y">Genome, Bacterial</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006090" 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