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NastyBugs: A simple method for extracting antimicrobial resistance information from metagenomes [version 1; referees: 2 approved with reservations]

Authors :
Hsinyi Tsang
Matthew Moss
Greg Fedewa
Sharif Farag
Daniel Quang
Alexey V. Rakov
Ben Busby
Author Affiliations :
<relatesTo>1</relatesTo>Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Gaithersburg, MD, 20850, USA<br /><relatesTo>2</relatesTo>Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA<br /><relatesTo>3</relatesTo>Bioinformatics, University of California, San Francisco, San Francisco, CA, 94158, USA<br /><relatesTo>4</relatesTo>Bioinformatics and Computational Biology, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA<br /><relatesTo>5</relatesTo>Department of Computer Science, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, 92617, USA<br /><relatesTo>6</relatesTo>Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA<br /><relatesTo>7</relatesTo>National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, 20894, USA
Source :
F1000Research. 6:1971
Publication Year :
2017
Publisher :
London, UK: F1000 Research Limited, 2017.

Abstract

Multidrug resistant bacteria are becoming a major threat to global public health. While there are many possible causes for this, there have so far been few adequate solutions to this problem. One of the major causes is a lack of clinical tools for efficient selection of an antibiotic in a reliable way. NastyBugs is a new program that can identify what type of antimicrobial resistance is most likely present in a metagenomic sample, which will allow for both smarter drug selection by clinicians and faster research in an academic environment.

Details

ISSN :
20461402
Volume :
6
Database :
F1000Research
Journal :
F1000Research
Notes :
[version 1; referees: 2 approved with reservations]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.12781.1
Document Type :
software-tool
Full Text :
https://doi.org/10.12688/f1000research.12781.1