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

ABSTRACT:

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 s

SUBMITTER: Drouin A 

PROVIDER: S-EPMC5037627 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature