If you have swab cultures from burn patients, what computational tools will you use to analyze the antimicrobial resistance among those cultures?

I am interested in carbapenem resistance or metallo-beta-lactamases.
Accepted
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Professor Dr. Osama Fekry Al Balah
A multi-layered computational approach involving sequence analysis, resistance prediction, and epidemiological tools is required to investigate antimicrobial resistance (AMR) in swab cultures from burn patients. 
 Key resources for identifying AMR: 
 To identify resistance genes in raw sequencing data or assembled genomes, you need ResFinder and CARD-RGI. CARD's RGI tool provides a comprehensive picture of all resistance genes, whereas ResFinder is only for acquired resistance genes. 
 The NCBI's AMRFinderPlus tool allows you to identify virulence factors, point mutations, and resistance genes in a single pipeline.

Sequence Pipeline Analysis: First, assess the raw read quality using Trimmomatic and FastQC. Next, assemble the genome using Unicycler or SPAdes. Centrifuge and Kraken2 are useful tools for identifying species in complicated clinical samples that might contain multiple organisms. 
 Specialized Resistance Analysis: PointFinder is a specialized tool used to identify resistance-associated chromosomal point mutations. 
 PlasmidFinder facilitates the identification of resistant plasmids, which is crucial for understanding horizontal gene transfer in burn units. 
 MLST typing tools for investigating outbreaks and determining the type of strain

Phenotype Prediction: CARD or AMR-meta's genotype-to-phenotype modules can use genomic information to predict an organism's likelihood of developing antibiotic resistance. Comparing these predictions with actual susceptibility testing is still crucial, though. 
 Combining and displaying data: 
 R/Bioconductor packages such as ARIBA for real-time resistance profiling from sequencing reads, PHYLOViZ or GrapeTree for phylogenetic analysis and outbreak detection, and gggenes for displaying resistance genes

Because these patients frequently have organisms like Pseudomonas and Acinetobacter that are resistant to multiple drugs, the burn unit setting is crucial in this situation. You should examine how these organisms evolve over time and incorporate species-specific resistance mechanisms into your analysis.

 

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Forensic Expert
Utilizaría el Análisis de Imágenes Forenses para estudiar placas obtenidas del cultivo de microorganismos resistentes (Pseudomonas, Acinetobacter y cualquier otro), junto con el apoyo de R y Python, para estudiar sus parámetros de crecimiento en el tiempo y aplicarlo al análisis de sus mecanismos de resistencia y mejorar los protocolos de aplicación de medicamentos antimicrobianos.
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RS

I assume that the isolates will be sequenced first.

From the viewpoint of AMR management, the traditional method of testing antibiotic sensitivity against a panel of key antibiotics has its merits because you can quickly intervene in individual cases if the isolates are *sensitive* against one or more key antibiotics used in medical practice.  The end-point for doctors is a cured patient in as little time as possible.

If the isolates turn out to be resistant to all of the routinely used antibiotics, bioinformatics can be leveraged to determine antibiotic resistance profiles.  In that case one could use the tools listed at the AMR data platforms – JPIAMR based on the required type of output, given the data available.



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Matheus
AMRFinder (AMRFinderplus) and CARD are very well established and reputable databases. RGI can also be used. Depending on the sequencing strategy used, you may want to pick one over the other. 
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Huseyin OZKAN
 it can be used microbial identification tools like Kraken2 and  Resfinder for resistance genes. Moreover, R or Phyton for the demonstration  the results. 

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