A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria
Identifieur interne : 000A96 ( Main/Curation ); précédent : 000A95; suivant : 000A97A 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 [ 1553-734X ] ; 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
Url:
DOI: 10.1371/journal.pcbi.1006434
PubMed: 30346947
PubMed Central: 6211763
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PMC:6211763Le document en format XML
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-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria</title>
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<term>DNA, Bacterial (physiology)</term>
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<term>Génome bactérien (génétique)</term>
<term>Génome bactérien (physiologie)</term>
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<term>Logiciel</term>
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<front><div type="abstract" xml:lang="en"><p>We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a <italic>k</italic>
-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 <italic>Klebsiella pneumoniae</italic>
isolates (virulence), 200 <italic>Pseudomonas aeruginosa</italic>
isolates (ciprofloxacin resistance) and 459 <italic>Clostridium difficile</italic>
isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the <italic>K</italic>
. <italic>pneumoniae</italic>
test set, 0.88 on the <italic>P</italic>
. <italic>aeruginosa</italic>
test set and 0.97 on the <italic>C</italic>
. <italic>difficile</italic>
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 (<ext-link ext-link-type="uri" xlink:href="https://github.com/bioinfo-ut/PhenotypeSeeker/">https://github.com/bioinfo-ut/PhenotypeSeeker/</ext-link>
).</p>
</div>
</front>
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