Evaluation of a fully automated bioinformatics tool to predict antibiotic resistance from MRSA genomes | Zendy

Narender Kumar | Zendy; Kathy E. Raven | Zendy; Beth Blane | Zendy; Danielle Leek | Zendy; Nicholas M. Brown | Zendy; Eugene Bragin | Zendy; Paul Rhodes | Zendy; Julian Parkhill | Zendy; Sharon J. Peacock | Zendy

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Evaluation of a fully automated bioinformatics tool to predict antibiotic resistance from MRSA genomes

Author(s) -

Narender Kumar,

Kathy E. Raven,

Beth Blane,

Danielle Leek,

Nicholas M. Brown,

Eugene Bragin,

Paul Rhodes,

Julian Parkhill,

Sharon J. Peacock

Publication year - 2020

Publication title -

journal of antimicrobial chemotherapy

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.124

H-Index - 194

eISSN - 1460-2091

pISSN - 0305-7453

DOI - 10.1093/jac/dkz570

Subject(s) - concordance , biology , antibiotic resistance , clinical microbiology , whole genome sequencing , phenotype , antibiotics , computational biology , genome , bioinformatics , genetics , microbiology and biotechnology , gene

The genetic prediction of phenotypic antibiotic resistance based on analysis of WGS data is becoming increasingly feasible, but a major barrier to its introduction into routine use is the lack of fully automated interpretation tools. Here, we report the findings of a large evaluation of the Next Gen Diagnostics (NGD) automated bioinformatics analysis tool to predict the phenotypic resistance of MRSA.

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