Contributed by Cory Schlesener, B.S.
Predicting bacterial isolate susceptibility for disease treatment and surveying resistance prevalence broadly is of great interest, especially as antibiotic resistance is increasing globally among clinical isolates. Screening sequence data for genes and alleles that confer antibiotic resistance has increased in availability but also in complexity over the past decade. There is now a myriad of bioinformatics tools available that utilize different interfaces and reference databases, as described (~50 listed) in the review by Hendriksen et al. (2019). Among the many options available, the database can be paired with the co-developed bioinformatic tool or independently developed tool, allowing for mix and match options. Additional complexity is added by the other programs used in setting up a bioinformatic pipeline to get raw sequence data pre-process to prepare for AMR search. Many AMR tools rely on annotating and searching genome assemblies, but more recent tools can operate off of local read alignments from sequencing reads, even from metagenomes. The differing AMR tools and pipelines lead to different outcomes in real-world usage, with further exacerbation with lower sequence quality, and outlined in comparisons by Doyle et al. (2020). It is important to do the proper pre-processing and input quality assessment for chosen pipeline and AMR tools. A helpful recent review, on the more common tools used, describes the differences among the latest version of tools and how they can be used. Additionally, the review provides an overview of other tools used to compile a pipeline (Lee et al. 2021).
References:
Hendriksen, Rene S., Valeria Bortolaia, Heather Tate, Gregory H. Tyson, Frank M. Aarestrup, and Patrick F. McDermott. “Using genomics to track global antimicrobial resistance.” Frontiers in public health 7 (2019): 242.
Doyle, Ronan M., Denise M. O’Sullivan, Sean D. Aller, Sebastian Bruchmann, Taane Clark, Andreu Coello Pelegrin, Martin Cormican et al. “Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.” Microbial genomics 6, no. 2 (2020).
Lee, Kihyun, Dae-Wi Kim, and Chang-Jun Cha. “Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data.” Journal of Microbiology 59, no. 3 (2021): 270-280.
