Mar 27, 2016

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

BioRxiv : the Preprint Server for Biology
Alexandre DrouinJacques Corbeil

Abstract

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.

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Mentioned in this Paper

Biological Markers
Biochemical Pathway
Mycoplasma pneumoniae
Genome
Antibiotic throat preparations
Antifungal Antibiotics, Topical
Klebsiella pneumoniae
Antibiotics, Gynecological
Genomics
Pseudomonas aeruginosa

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