PT  - JOURNAL ARTICLE
AU  - Alexandre Drouin
AU  - Sébastien Giguère
AU  - Maxime Déraspe
AU  - Mario Marchand
AU  - Michael Tyers
AU  - Vivian G. Loo
AU  - Anne-Marie Bourgault
AU  - François Laviolette
AU  - Jacques Corbeil
TI  - Predictive Computational Phenotyping and Biomarker Discovery Using Reference-Free Genome Comparisons
AID  - 10.1101/045153
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 045153
4099  - http://biorxiv.org/content/early/2016/03/27/045153.short
4100  - http://biorxiv.org/content/early/2016/03/27/045153.full
AB  - The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a new reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa and S. pneumoniae. We show that the obtained models are accurate and that they highlight biologically relevant biomarkers, while providing insight into the process of antibiotic resistance acquisition.