Abstract：
BACKGROUND: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 k-mer based genotyping scheme and a logistic regression model, thereby combining several k-mers into a probabilistic model. To identify a small yet predictive set of k-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. RESULTS: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 k-mers per antibiotic, hence are remarkably easy and fast to evaluate on new genomes (from raw reads to assemblies). CONCLUSION:Our proof of concept therefore demonstrates that stability selection is a powerful approach to investigate bacterial genotype-phenotype relationships.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Mahé P,Tournoud Mdoi
10.1186/s12859-018-2403-zsubject
Has Abstractpub_date
2018-10-17 00:00:00pages
383issue
1issn
1471-2105pii
10.1186/s12859-018-2403-zjournal_volume
19pub_type
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