A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events
Identifieur interne : 000043 ( France/Analysis ); précédent : 000042; suivant : 000044A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events
Auteurs : Magali Jaillard [France] ; Leandro Lima [France] ; Maud Tournoud [France] ; Pierre Mahé [France] ; Alex Van Belkum [France] ; Vincent Lacroix [France] ; Laurent Jacob [France]Source :
- PLoS Genetics [ 1553-7390 ] ; 2018.
Descripteurs français
- KwdFr :
- ADN bactérien (génétique), Analyse de séquence d'ADN, Bases de données génétiques, Génome bactérien, Infographie, Logiciel, Modèles génétiques, Mycobacterium tuberculosis (), Mycobacterium tuberculosis (génétique), Phénotype, Pseudomonas aeruginosa (), Pseudomonas aeruginosa (génétique), Résistance bactérienne aux médicaments (génétique), Staphylococcus aureus (), Staphylococcus aureus (génétique), Séquences répétées dispersées, Variation génétique, Étude d'association pangénomique ().
- MESH :
- génétique : ADN bactérien, Mycobacterium tuberculosis, Pseudomonas aeruginosa, Résistance bactérienne aux médicaments, Staphylococcus aureus.
- Analyse de séquence d'ADN, Bases de données génétiques, Génome bactérien, Infographie, Logiciel, Modèles génétiques, Mycobacterium tuberculosis, Phénotype, Pseudomonas aeruginosa, Staphylococcus aureus, Séquences répétées dispersées, Variation génétique, Étude d'association pangénomique.
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
Url:
DOI: 10.1371/journal.pgen.1007758
PubMed: 30419019
PubMed Central: 6258240
Affiliations:
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PMC:6258240Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>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 <italic>Mycobacterium tuberculosis</italic>
, and genes acquired by horizontal transfer in <italic>Staphylococcus aureus</italic>
and <italic>Pseudomonas aeruginosa</italic>
—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 <ext-link ext-link-type="uri" xlink:href="https://gitlab.com/leoisl/dbgwas">https://gitlab.com/leoisl/dbgwas</ext-link>
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</div1>
</back>
</TEI>
<affiliations><list><country><li>France</li>
</country>
<region><li>Auvergne-Rhône-Alpes</li>
<li>Rhône-Alpes</li>
</region>
<settlement><li>Villeurbanne</li>
</settlement>
</list>
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<name sortKey="Jacob, Laurent" sort="Jacob, Laurent" uniqKey="Jacob L" first="Laurent" last="Jacob">Laurent Jacob</name>
<name sortKey="Jaillard, Magali" sort="Jaillard, Magali" uniqKey="Jaillard M" first="Magali" last="Jaillard">Magali Jaillard</name>
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<name sortKey="Lacroix, Vincent" sort="Lacroix, Vincent" uniqKey="Lacroix V" first="Vincent" last="Lacroix">Vincent Lacroix</name>
<name sortKey="Lima, Leandro" sort="Lima, Leandro" uniqKey="Lima L" first="Leandro" last="Lima">Leandro Lima</name>
<name sortKey="Lima, Leandro" sort="Lima, Leandro" uniqKey="Lima L" first="Leandro" last="Lima">Leandro Lima</name>
<name sortKey="Mahe, Pierre" sort="Mahe, Pierre" uniqKey="Mahe P" first="Pierre" last="Mahé">Pierre Mahé</name>
<name sortKey="Tournoud, Maud" sort="Tournoud, Maud" uniqKey="Tournoud M" first="Maud" last="Tournoud">Maud Tournoud</name>
<name sortKey="Van Belkum, Alex" sort="Van Belkum, Alex" uniqKey="Van Belkum A" first="Alex" last="Van Belkum">Alex Van Belkum</name>
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</affiliations>
</record>
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