GalileoTM AMR Detection Software

Antimicrobial Resistance Detection

Galileo™ AMR is a unique, closed-source system which allows the creation of an archive of relevant mobile elements and identification of these elements in bacterial DNA sequences. The repository of antibiotic resistance genes is available to the wider research community to allow researchers to annotate sequences associated with bacterial resistance and contribute new entries to the database as these are found.

Powered by two AMR databases and a genomic detection engine. Galileo™ AMR is designed to empower those who perform AMR sequence research and analysis.

Galileo™ AMR incorporates advanced analytics in a user-friendly interface with the latest scientific knowledge from the research community to achieve

 

      • Fast and reliable detection of AMR genes in Gram-negative bacteria

      • Precise annotation of AMR genes and mobile elements in DNA sequences of any length

      • Reliable annotation of multi-drug resistant organisms

      • Detailed results in sequence annotation diagrams

      • Identification of partial annotations

Galileo TM AMR Workflow

Fast and Precise Annotations: Galileo™ AMR’s advanced detection engine 3,4 provides quick and reliable annotations for antibiotic resistance genes, cassettes, and other mobile elements. Simply upload a sequence and get detailed, yet easy to understand results with positions, orientations, diagrams, and more.

Get Access to Galileo™ AMR

Please contact us directly to receive a customized quote for you or your organization:

Publications

1. Tsafnat G, Copty J, Partridge SR. RAC: Repository of Antibiotic resistance Cassettes. Database. 2011; bar054. doi:10.1093/database/bar054/470201

2. Partridge SR, Tsafnat G. Automated annotation of mobile antibiotic resistance in Gram-negative bacteria: the Multiple Antibiotic Resistance Annotator (MARA) and database. Journal of Antimicrobial Chemotherapy. 2018; dkx513. doi: https://doi.org/10.1093/jac/dkx513

3. Tsafnat G, Coiera E, Partridge SR, Schaeffer J, Iredell JR. Context-driven discovery of gene cassettes in mobile integrons using a computational grammar. BMC Bioinformatics. 2009; 10(1):281. doi:10.1186/1471-2105-10-281

4. Tsafnat G, Schaeffer J, Clayphan A, Iredell JR, Partridge SR, Coiera E. Computational inference of grammars for larger-than-gene structures from annotated gene sequences. Bioinformatics. 2011; 27 (6): 791-796. doi:10.1093/bioinformatics/btr036

For Research Use Only. Not for use in diagnostics or diagnostic procedures.