Literature DB >> 27875856

Antimicrobial resistance surveillance in the genomic age.

Andrew G McArthur1, Kara K Tsang1.   

Abstract

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.
© 2016 New York Academy of Sciences.

Entities:  

Keywords:  antimicrobial resistance; biocuration; bioinformatics; genomics

Mesh:

Substances:

Year:  2016        PMID: 27875856     DOI: 10.1111/nyas.13289

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  18 in total

1.  Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella.

Authors:  Marcus Nguyen; S Wesley Long; Patrick F McDermott; Randall J Olsen; Robert Olson; Rick L Stevens; Gregory H Tyson; Shaohua Zhao; James J Davis
Journal:  J Clin Microbiol       Date:  2019-01-30       Impact factor: 5.948

Review 2.  Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective.

Authors:  Jee In Kim; Finlay Maguire; Kara K Tsang; Theodore Gouliouris; Sharon J Peacock; Tim A McAllister; Andrew G McArthur; Robert G Beiko
Journal:  Clin Microbiol Rev       Date:  2022-05-25       Impact factor: 50.129

Review 3.  Whole-Genome Sequencing of Bacterial Pathogens: the Future of Nosocomial Outbreak Analysis.

Authors:  Scott Quainoo; Jordy P M Coolen; Sacha A F T van Hijum; Martijn A Huynen; Willem J G Melchers; Willem van Schaik; Heiman F L Wertheim
Journal:  Clin Microbiol Rev       Date:  2017-10       Impact factor: 26.132

Review 4.  Sequencing-based methods and resources to study antimicrobial resistance.

Authors:  Manish Boolchandani; Alaric W D'Souza; Gautam Dantas
Journal:  Nat Rev Genet       Date:  2019-06       Impact factor: 53.242

5.  Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study.

Authors:  Chih-Yu Chen; Clifford G Clark; Stacie Langner; David A Boyd; Amrita Bharat; Stuart J McCorrister; Andrew G McArthur; Morag R Graham; Garrett R Westmacott; Gary Van Domselaar
Journal:  Proteomics Clin Appl       Date:  2020-02-28       Impact factor: 3.494

6.  Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation.

Authors:  Abu Sayed Chowdhury; Douglas R Call; Shira L Broschat
Journal:  Sci Rep       Date:  2019-10-09       Impact factor: 4.379

Review 7.  Using Genomics to Track Global Antimicrobial Resistance.

Authors:  Rene S Hendriksen; Valeria Bortolaia; Heather Tate; Gregory H Tyson; Frank M Aarestrup; Patrick F McDermott
Journal:  Front Public Health       Date:  2019-09-04

8.  Antibiotic resistomes of healthy pig faecal metagenomes.

Authors:  Aoife Joyce; Charley G P McCarthy; Sinead Murphy; Fiona Walsh
Journal:  Microb Genom       Date:  2019-05-15

9.  DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Authors:  Gustavo Arango-Argoty; Emily Garner; Amy Pruden; Lenwood S Heath; Peter Vikesland; Liqing Zhang
Journal:  Microbiome       Date:  2018-02-01       Impact factor: 14.650

10.  CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database.

Authors:  Brian P Alcock; Amogelang R Raphenya; Tammy T Y Lau; Kara K Tsang; Mégane Bouchard; Arman Edalatmand; William Huynh; Anna-Lisa V Nguyen; Annie A Cheng; Sihan Liu; Sally Y Min; Anatoly Miroshnichenko; Hiu-Ki Tran; Rafik E Werfalli; Jalees A Nasir; Martins Oloni; David J Speicher; Alexandra Florescu; Bhavya Singh; Mateusz Faltyn; Anastasia Hernandez-Koutoucheva; Arjun N Sharma; Emily Bordeleau; Andrew C Pawlowski; Haley L Zubyk; Damion Dooley; Emma Griffiths; Finlay Maguire; Geoff L Winsor; Robert G Beiko; Fiona S L Brinkman; William W L Hsiao; Gary V Domselaar; Andrew G McArthur
Journal:  Nucleic Acids Res       Date:  2020-01-08       Impact factor: 16.971

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