Back to Search Start Over

PATRIC as a unique resource for studying antimicrobial resistance

Authors :
Alice R. Wattam
Andrew S. Warren
James J. Davis
Rick Stevens
Eric K. Nordberg
Chunhong Mao
Svetlana Gerdes
Fangfang Xia
John Santerre
Thomas Brettin
Dustin Machi
Ramy K. Aziz
Margo VanOeffelen
Terry Disz
Gordon D. Pusch
Maulik Shukla
Rida Assaf
Hyunseung Yoo
Emily M. Dietrich
Christopher Bun
Gary J. Olsen
Veronika Vonstein
Dionysios A. Antonopoulos
Neal Conrad
Daniel E. Murphy-Olson
Bruce Parrello
Ronald W. Kenyon
Ross Overbeek
Robert Olson
Source :
Briefings in Bioinformatics
Publication Year :
2017
Publisher :
Oxford University Press (OUP), 2017.

Abstract

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other ‘omic’ data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Details

ISSN :
14774054 and 14675463
Volume :
20
Database :
OpenAIRE
Journal :
Briefings in Bioinformatics
Accession number :
edsair.doi.dedup.....f6b104850e808ddba266e936e77222f5