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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, Mario Marchand, Michael Tyers, Vivian G. Loo, Anne-Marie Bourgault, François Laviolette, Jacques Corbeil

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 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/ ). More... »

PAGES

754

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12864-016-2889-6

DOI

http://dx.doi.org/10.1186/s12864-016-2889-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052400167

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27671088


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12864-016-2889-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12864-016-2889-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12864-016-2889-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12864-016-2889-6'


 

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