Currently submitted to: JMIR Bioinformatics and Biotechnology
Date Submitted: Jul 5, 2026
Open Peer Review Period: Jul 20, 2026 - Sep 14, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Identification and Characterization of Antimicrobial Resistance Genes in an Assembled Staphylococcus aureus Genome Using a Bioinformatics Workflow
ABSTRACT
Background:
Antimicrobial resistance (AMR) has become a major global public health challenge, reducing the effectiveness of antibiotics and increasing the burden of infectious diseases. Bioinformatics tools enable rapid identification of resistance genes from bacterial whole-genome sequencing data, supporting surveillance and infection control.
Objective:
To identify and characterize antimicrobial resistance genes, plasmid replicons, and sequence type in an assembled Staphylococcus aureus genome using a Galaxy-based bioinformatics workflow.
Methods:
Raw sequencing reads were quality-filtered and assembled into contigs. Genome annotation was performed using Bakta. Antimicrobial resistance genes, plasmid replicons, and multilocus sequence typing (MLST) were identified using StarAMR. Sequence alignment and mapping were carried out using Bowtie2, and the results were summarized through graphical visualization
Results:
The analysis identified several antimicrobial resistance genes, including aac(6')-aph(2''), aadD, ant(9)-Ia, bleO, erm(A), mecA, and tet(M). Plasmid replicons rep16, rep19, rep22, rep5a, and repUS43 were detected. MLST analysis identified the isolate as Sequence Type 764 (ST764) of Staphylococcus aureus. Most detected genes showed approximately 99.9–100% sequence identity, indicating highly reliable identification.
Conclusions:
The bioinformatics workflow successfully identified clinically important antimicrobial resistance determinants in the assembled Staphylococcus aureus genome. The presence of the mecA gene together with multiple additional resistance genes indicates a multidrug-resistant profile and demonstrates the usefulness of genome-based AMR surveillance for research and public health. Clinical Trial: Trial Registration: Not applicable (bioinformatics / in-silico study; no human participants or randomized trial involved).
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