Antimicrobial resistance surveillance in the genomic age

• Published in: Annals of the New York Academy of Sciences Vol. 1388; no. 1; pp. 78 - 91
• Main Authors: McArthur, Andrew G., Tsang, Kara K.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2017
• Subjects: Algorithms; Animals; Anti-Infective Agents; Antibiotic resistance; Antibiotics; Antimicrobial agents; antimicrobial resistance; biocuration; Bioinformatics; Computational Biology - methods; Computational Biology - trends; Drug resistance; Drug Resistance - genetics; Genome; Genomes; genomics; Genomics - methods; Genomics - trends; High-Throughput Nucleotide Sequencing - methods; High-Throughput Nucleotide Sequencing - trends; Humans; Infectious diseases; Information technology; Models, Genetic; Public health; Software; Surveillance; genomics; antimicrobial resistance; biocuration; bioinformatics
• ISSN: 0077-8923; 1749-6632; 1749-6632
• DOI: 10.1111/nyas.13289

The loss of effective antimicrobials is reducing our ability to protect the global population from infectious disease. However, the field of antibiotic drug discovery and the public health monitoring of antimicrobial resistance (AMR) is beginning to exploit the power of genome and metagenome sequencing. The creation of novel AMR bioinformatics tools and databases and their continued development will advance our understanding of the molecular mechanisms and threat severity of antibiotic resistance, while simultaneously improving our ability to accurately predict and screen for antibiotic resistance genes within environmental, agricultural, and clinical settings. To do so, efforts must be focused toward exploiting the advancements of genome sequencing and information technology. Currently, AMR bioinformatics software and databases reflect different scopes and functions, each with its own strengths and weaknesses. A review of the available tools reveals common approaches and reference data but also reveals gaps in our curated data, models, algorithms, and data‐sharing tools that must be addressed to conquer the limitations and areas of unmet need within the AMR research field before DNA sequencing can be fully exploited for AMR surveillance and improved clinical outcomes.