Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection
Identifieur interne : 000853 ( Main/Exploration ); précédent : 000852; suivant : 000854Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection
Auteurs : Pierre Mahé ; Maud TournoudSource :
- BMC Bioinformatics [ 1471-2105 ] ; 2018.
Descripteurs français
- KwdFr :
- Algorithmes, Antibactériens (pharmacologie), Bases de données génétiques, Courbe ROC, Génome bactérien, Modèles génétiques, Modèles logistiques, Mycobacterium tuberculosis (), Mycobacterium tuberculosis (génétique), Reproductibilité des résultats, Résistance bactérienne aux médicaments (), Résistance bactérienne aux médicaments (génétique), Staphylococcus aureus (), Staphylococcus aureus (génétique), Séquence nucléotidique.
- MESH :
- génétique : Mycobacterium tuberculosis, Résistance bactérienne aux médicaments, Staphylococcus aureus.
- pharmacologie : Antibactériens.
- Algorithmes, Bases de données génétiques, Courbe ROC, Génome bactérien, Modèles génétiques, Modèles logistiques, Mycobacterium tuberculosis, Reproductibilité des résultats, Résistance bactérienne aux médicaments, Staphylococcus aureus, Séquence nucléotidique.
English descriptors
- KwdEn :
- Algorithms, Anti-Bacterial Agents (pharmacology), Base Sequence, Databases, Genetic, Drug Resistance, Bacterial (drug effects), Drug Resistance, Bacterial (genetics), Genome, Bacterial, Logistic Models, Models, Genetic, Mycobacterium tuberculosis (drug effects), Mycobacterium tuberculosis (genetics), ROC Curve, Reproducibility of Results, Staphylococcus aureus (drug effects), Staphylococcus aureus (genetics), Whole Genome Sequencing.
- MESH :
- chemical , pharmacology : Anti-Bacterial Agents.
- drug effects : Drug Resistance, Bacterial, Mycobacterium tuberculosis, Staphylococcus aureus.
- genetics : Drug Resistance, Bacterial, Mycobacterium tuberculosis, Staphylococcus aureus.
- Algorithms, Base Sequence, Databases, Genetic, Genome, Bacterial, Logistic Models, Models, Genetic, ROC Curve, Reproducibility of Results, Whole Genome Sequencing.
Abstract
Several studies demonstrated the feasibility of predicting bacterial antibiotic resistance phenotypes from whole-genome sequences, the prediction process usually amounting to detecting the presence of genes involved in antibiotic resistance mechanisms, or of specific mutations, previously identified from a training panel of strains, within these genes. We address the problem from the supervised statistical learning perspective, not relying on prior information about such resistance factors. We rely on a
Using public datasets, we applied the resulting classifiers to two bacterial species and achieved predictive performance equivalent to state of the art. The models are extremely sparse, involving 1 to 8
Our proof of concept therefore demonstrates that stability selection is a powerful approach to investigate bacterial genotype-phenotype relationships.
The online version of this article (10.1186/s12859-018-2403-z) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-018-2403-z
PubMed: 30332990
PubMed Central: 6192184
Affiliations:
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Le document en format XML
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type="wicri:Area/Main/Merge">000856</idno><idno type="wicri:Area/Main/Curation">000853</idno><idno type="wicri:Area/Main/Exploration">000853</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Predicting bacterial resistance from whole-genome sequences using <italic>k</italic>-mers and stability selection</title><author><name sortKey="Mahe, Pierre" sort="Mahe, Pierre" uniqKey="Mahe P" first="Pierre" last="Mahé">Pierre Mahé</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Tournoud, Maud" sort="Tournoud, Maud" uniqKey="Tournoud M" first="Maud" last="Tournoud">Maud Tournoud</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Anti-Bacterial Agents (pharmacology)</term><term>Base Sequence</term><term>Databases, Genetic</term><term>Drug Resistance, Bacterial (drug effects)</term><term>Drug Resistance, Bacterial (genetics)</term><term>Genome, Bacterial</term><term>Logistic Models</term><term>Models, Genetic</term><term>Mycobacterium tuberculosis (drug effects)</term><term>Mycobacterium tuberculosis (genetics)</term><term>ROC Curve</term><term>Reproducibility of Results</term><term>Staphylococcus aureus (drug effects)</term><term>Staphylococcus aureus (genetics)</term><term>Whole Genome Sequencing</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Antibactériens (pharmacologie)</term><term>Bases de données génétiques</term><term>Courbe ROC</term><term>Génome bactérien</term><term>Modèles génétiques</term><term>Modèles logistiques</term><term>Mycobacterium tuberculosis ()</term><term>Mycobacterium tuberculosis (génétique)</term><term>Reproductibilité des résultats</term><term>Résistance bactérienne aux médicaments ()</term><term>Résistance bactérienne aux médicaments (génétique)</term><term>Staphylococcus aureus ()</term><term>Staphylococcus aureus (génétique)</term><term>Séquence nucléotidique</term></keywords><keywords scheme="MESH" type="chemical" qualifier="pharmacology" xml:lang="en"><term>Anti-Bacterial Agents</term></keywords><keywords scheme="MESH" qualifier="drug effects" xml:lang="en"><term>Drug Resistance, Bacterial</term><term>Mycobacterium tuberculosis</term><term>Staphylococcus aureus</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Drug Resistance, Bacterial</term><term>Mycobacterium tuberculosis</term><term>Staphylococcus aureus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Mycobacterium tuberculosis</term><term>Résistance bactérienne aux médicaments</term><term>Staphylococcus aureus</term></keywords><keywords scheme="MESH" qualifier="pharmacologie" xml:lang="fr"><term>Antibactériens</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Base Sequence</term><term>Databases, Genetic</term><term>Genome, Bacterial</term><term>Logistic Models</term><term>Models, Genetic</term><term>ROC Curve</term><term>Reproducibility of Results</term><term>Whole Genome Sequencing</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Bases de données génétiques</term><term>Courbe ROC</term><term>Génome bactérien</term><term>Modèles génétiques</term><term>Modèles logistiques</term><term>Mycobacterium tuberculosis</term><term>Reproductibilité des résultats</term><term>Résistance bactérienne aux médicaments</term><term>Staphylococcus aureus</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Several studies demonstrated the feasibility of predicting bacterial antibiotic resistance phenotypes from whole-genome sequences, the prediction process usually amounting to detecting the presence of genes involved in antibiotic resistance mechanisms, or of specific mutations, previously identified from a training panel of strains, within these genes. We address the problem from the supervised statistical learning perspective, not relying on prior information about such resistance factors. We rely on a <italic>k</italic>-mer based genotyping scheme and a logistic regression model, thereby combining several <italic>k</italic>-mers into a probabilistic model. To identify a small yet predictive set of <italic>k</italic>-mers, we rely on the stability selection approach (Meinshausen et al., J R Stat Soc Ser B 72:417–73, 2010), that consists in penalizing logistic regression models with a Lasso penalty, coupled with extensive resampling procedures.</p></sec><sec><title>Results</title><p>Using public datasets, we applied the resulting classifiers to two bacterial species and achieved predictive performance equivalent to state of the art. The models are extremely sparse, involving 1 to 8 <italic>k</italic>-mers per antibiotic, hence are remarkably easy and fast to evaluate on new genomes (from raw reads to assemblies).</p></sec><sec><title>Conclusion</title><p>Our proof of concept therefore demonstrates that stability selection is a powerful approach to investigate bacterial genotype-phenotype relationships.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (10.1186/s12859-018-2403-z) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Loman, Nj" uniqKey="Loman N">NJ Loman</name></author><author><name sortKey="Constantinidou, C" uniqKey="Constantinidou C">C Constantinidou</name></author><author><name sortKey="Chan, Jz" uniqKey="Chan J">JZ Chan</name></author><author><name sortKey="Halachev, M" uniqKey="Halachev M">M Halachev</name></author><author><name sortKey="Sergeant, M" uniqKey="Sergeant M">M Sergeant</name></author><author><name sortKey="Penn, Cw" uniqKey="Penn C">CW Penn</name></author><author><name sortKey="Robinson, Er" uniqKey="Robinson E">ER Robinson</name></author><author><name sortKey="Pallen, Mj" uniqKey="Pallen M">MJ Pallen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chan, Jacqueline Z M" uniqKey="Chan J">Jacqueline Z-M Chan</name></author><author><name sortKey="Pallen, Mark J" uniqKey="Pallen M">Mark J Pallen</name></author><author><name sortKey="Oppenheim, Beryl" uniqKey="Oppenheim B">Beryl Oppenheim</name></author><author><name sortKey="Constantinidou, Chrystala" uniqKey="Constantinidou C">Chrystala Constantinidou</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bertelli, C" uniqKey="Bertelli C">C. 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McCullagh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dundar, M" uniqKey="Dundar M">M Dundar</name></author><author><name sortKey="Krishnapuram, B" uniqKey="Krishnapuram B">B Krishnapuram</name></author><author><name sortKey="Bi, J" uniqKey="Bi J">J Bi</name></author><author><name sortKey="Rao, Rb" uniqKey="Rao R">RB Rao</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Votintseva, Antonina A" uniqKey="Votintseva A">Antonina A. Votintseva</name></author><author><name sortKey="Bradley, Phelim" uniqKey="Bradley P">Phelim Bradley</name></author><author><name sortKey="Pankhurst, Louise" uniqKey="Pankhurst L">Louise Pankhurst</name></author><author><name sortKey="Del Ojo Elias, Carlos" uniqKey="Del Ojo Elias C">Carlos del Ojo Elias</name></author><author><name sortKey="Loose, Matthew" uniqKey="Loose M">Matthew Loose</name></author><author><name sortKey="Nilgiriwala, Kayzad" uniqKey="Nilgiriwala K">Kayzad Nilgiriwala</name></author><author><name sortKey="Chatterjee, Anirvan" uniqKey="Chatterjee A">Anirvan Chatterjee</name></author><author><name sortKey="Smith, E Grace" uniqKey="Smith E">E. Grace Smith</name></author><author><name sortKey="Sanderson, Nicolas" uniqKey="Sanderson N">Nicolas Sanderson</name></author><author><name sortKey="Walker, Timothy M" uniqKey="Walker T">Timothy M. Walker</name></author><author><name sortKey="Morgan, Marcus R" uniqKey="Morgan M">Marcus R. Morgan</name></author><author><name sortKey="Wyllie, David H" uniqKey="Wyllie D">David H. Wyllie</name></author><author><name sortKey="Walker, A Sarah" uniqKey="Walker A">A. Sarah Walker</name></author><author><name sortKey="Peto, Tim E A" uniqKey="Peto T">Tim E. A. Peto</name></author><author><name sortKey="Crook, Derrick W" uniqKey="Crook D">Derrick W. Crook</name></author><author><name sortKey="Iqbal, Zamin" uniqKey="Iqbal Z">Zamin Iqbal</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list></list><tree><noCountry><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></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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