A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.
Identifieur interne : 000735 ( PubMed/Corpus ); précédent : 000734; suivant : 000736A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.
Auteurs : Magali Jaillard ; Leandro Lima ; Maud Tournoud ; Pierre Mahé ; Alex Van Belkum ; Vincent Lacroix ; Laurent JacobSource :
- PLoS genetics [ 1553-7404 ] ; 2018.
English descriptors
- KwdEn :
- Computer Graphics, DNA, Bacterial (genetics), Databases, Genetic, Drug Resistance, Bacterial (genetics), Genetic Variation, Genome, Bacterial, Genome-Wide Association Study (methods), Genome-Wide Association Study (statistics & numerical data), Interspersed Repetitive Sequences, Models, Genetic, Mycobacterium tuberculosis (drug effects), Mycobacterium tuberculosis (genetics), Phenotype, Pseudomonas aeruginosa (drug effects), Pseudomonas aeruginosa (genetics), Sequence Analysis, DNA, Software, Staphylococcus aureus (drug effects), Staphylococcus aureus (genetics).
- MESH :
- chemical , genetics : DNA, Bacterial.
- drug effects : Mycobacterium tuberculosis, Pseudomonas aeruginosa, Staphylococcus aureus.
- genetics : Drug Resistance, Bacterial, Mycobacterium tuberculosis, Pseudomonas aeruginosa, Staphylococcus aureus.
- methods : Genome-Wide Association Study.
- statistics & numerical data : Genome-Wide Association Study.
- Computer Graphics, Databases, Genetic, Genetic Variation, Genome, Bacterial, Interspersed Repetitive Sequences, Models, Genetic, Phenotype, Sequence Analysis, DNA, Software.
Abstract
Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas.
DOI: 10.1371/journal.pgen.1007758
PubMed: 30419019
Links to Exploration step
pubmed:30419019Le document en format XML
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France.</nlm:affiliation></affiliation></author><author><name sortKey="Lima, Leandro" sort="Lima, Leandro" uniqKey="Lima L" first="Leandro" last="Lima">Leandro Lima</name><affiliation><nlm:affiliation>Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558 F-69622 Villeurbanne, France.</nlm:affiliation></affiliation></author><author><name sortKey="Tournoud, Maud" sort="Tournoud, Maud" uniqKey="Tournoud M" first="Maud" last="Tournoud">Maud Tournoud</name><affiliation><nlm:affiliation>bioMérieux, Marcy l'Étoile, France.</nlm:affiliation></affiliation></author><author><name sortKey="Mahe, Pierre" sort="Mahe, Pierre" uniqKey="Mahe P" first="Pierre" last="Mahé">Pierre Mahé</name><affiliation><nlm:affiliation>bioMérieux, Marcy l'Étoile, France.</nlm:affiliation></affiliation></author><author><name sortKey="Van Belkum, Alex" sort="Van Belkum, Alex" uniqKey="Van Belkum A" first="Alex" last="Van Belkum">Alex Van 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DNA</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30419019</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>26</Day></DateCompleted><DateRevised><Year>2019</Year><Month>02</Month><Day>26</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7404</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>11</Issue><PubDate><Year>2018</Year><Month>11</Month></PubDate></JournalIssue><Title>PLoS genetics</Title><ISOAbbreviation>PLoS Genet.</ISOAbbreviation></Journal><ArticleTitle>A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.</ArticleTitle><Pagination><MedlinePgn>e1007758</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pgen.1007758</ELocationID><Abstract><AbstractText>Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Jaillard</LastName><ForeName>Magali</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0001-7010-1921</Identifier><AffiliationInfo><Affiliation>bioMérieux, Marcy l'Étoile, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558 F-69622 Villeurbanne, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lima</LastName><ForeName>Leandro</ForeName><Initials>L</Initials><Identifier Source="ORCID">0000-0001-8976-2762</Identifier><AffiliationInfo><Affiliation>Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558 F-69622 Villeurbanne, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>EPI ERABLE - Inria Grenoble, Rhône-Alpes, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tournoud</LastName><ForeName>Maud</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0003-2459-9831</Identifier><AffiliationInfo><Affiliation>bioMérieux, Marcy l'Étoile, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mahé</LastName><ForeName>Pierre</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>bioMérieux, Marcy l'Étoile, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>van Belkum</LastName><ForeName>Alex</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>bioMérieux, Marcy l'Étoile, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lacroix</LastName><ForeName>Vincent</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558 F-69622 Villeurbanne, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>EPI ERABLE - Inria Grenoble, Rhône-Alpes, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jacob</LastName><ForeName>Laurent</ForeName><Initials>L</Initials><Identifier Source="ORCID">0000-0002-7826-2719</Identifier><AffiliationInfo><Affiliation>Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558 F-69622 Villeurbanne, France.</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>11</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Genet</MedlineTA><NlmUniqueID>101239074</NlmUniqueID><ISSNLinking>1553-7390</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D004269">DNA, Bacterial</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003196" MajorTopicYN="N">Computer Graphics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004269" MajorTopicYN="N">DNA, Bacterial</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D030541" MajorTopicYN="N">Databases, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D024881" MajorTopicYN="N">Drug Resistance, Bacterial</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014644" MajorTopicYN="N">Genetic Variation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016680" MajorTopicYN="Y">Genome, Bacterial</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055106" MajorTopicYN="N">Genome-Wide Association Study</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020071" MajorTopicYN="N">Interspersed Repetitive Sequences</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008957" MajorTopicYN="N">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009169" MajorTopicYN="N">Mycobacterium tuberculosis</DescriptorName><QualifierName UI="Q000187" MajorTopicYN="N">drug effects</QualifierName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010641" MajorTopicYN="N">Phenotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011550" MajorTopicYN="N">Pseudomonas aeruginosa</DescriptorName><QualifierName UI="Q000187" MajorTopicYN="N">drug effects</QualifierName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="N">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013211" MajorTopicYN="N">Staphylococcus aureus</DescriptorName><QualifierName UI="Q000187" MajorTopicYN="N">drug effects</QualifierName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading></MeshHeadingList><CoiStatement>I have read the journal’s policy and the authors of this manuscript have the following competing interests: MJ, MT, PM and AvB are employees of bioMérieux, a company that develops and sells diagnostic tests in the field of infectious diseases. However, the study was designed and executed in an open manner and the presented method as well as all data generated have been deposited in the public domain, also resulting in the current publication.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>06</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>10</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>11</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>11</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>2</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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