RT Journal Article
SR Electronic
T1 Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 045153
DO 10.1101/045153
A1 Alexandre Drouin
A1 Sébastien Giguère
A1 Maxime Déraspe
A1 Mario Marchand
A1 Michael Tyers
A1 Vivian G. Loo
A1 Anne-Marie Bourgault
A1 François Laviolette
A1 Jacques Corbeil
YR 2016
UL http://biorxiv.org/content/early/2016/08/31/045153.abstract
AB Background The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a 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.Results The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery.Conclusions Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github.com/aldro61/kover/).