Lab Matters Summer 2022 | страница 21

Comprehensive Antibiotic Resistance Database ( CARD ) or ResFinder . Developing an automated bioinformatics pipeline that can simplify this process will further lower the adoption barrier for WGS in all laboratories . This will also widen the bandwidth of bioinformaticians to do more useful downstream investigations with the genomic data .

Once a critical mass of laboratories are able to adopt WGS of AR bacterial isolates and share their data in near real-time , it will only be a matter of time before publicly curated AR / AST databases accumulate sufficient data to utilize machine learning tools to improve

References :

1 . CDC , Centers for Disease Control and Prevention ( 2019 ). Antibiotic Resistance Threats in the United States . Retrieved March 3 , 2022 from https :// www . cdc . gov / drugresistance / pdf / threats-report / 2019-ar-threats-report-508 . pdf

2 . WHO , World Health Organization , Global Antimicrobial Resistance and Use Surveillance System ( GLASS ) ( 2020 ). GLASS – Whole-genome sequencing for surveillance of antimicrobial resistance . Retrieved March 10 , 2022 from https :// www . who . int / publications / i / item / 9789240011007

3 . Hess , JF , Kohl , TA , Kotrová , M , Rönsch , K , Paprotka , T , Mohr , V , et al . ( 2020 ). Library preparation for next generation sequencing : A review of automation strategies . Biotechnology Advances 41 ( 2020 ) 107537

4 . Hendriksen , RS , Bortolaia , V , Tate , H , Tyson , GH , Aarestrup , FM and McDermott , PF . Using Genomics to Track Global Antimicrobial Resistance . Frontiers in Public Health , 04 September 2019 , https :// doi . org / 10.3389 / fpubh . 2019.00242

5 . Nguyen M , Long SW , McDermott PF , Olsen RJ , Olson R , Stevens RL , et al . Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal salmonella . J Clin Microbiol . ( 2019 ) 57 : e01260 – 18 . doi : 10.1128 / JCM . 01260-18

predictive modeling to connect genotypes to putative phenotypes in support of treatment , predicting resistance profiles , helping to engineer new antibiotics and decreasing the time needed to identify the source of an outbreak . ⁵ By democratizing genomics through lowering costs , integration of automation with bioinformatics and expanding public databases , we can establish a revolutionary infrastructure to deliver genomic epidemiological insights to meet the needs of scientists in the fight against pathogens . Through a concerted effort to deliver a simplified yet powerful automation platform , a future without multi-drug resistant bacteria is within reach . n

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