Genomic Analysis and AMR Detection in Pathogenic Bacteria

Dear Colleagues,

The investigation of bacterial genomes is challenging. This is particularly true for mechanisms behind antibiotic resistance mechanisms. Multidrug-resistant bacteria are an emergent global disease and a major public health problem. The increasing availability of NGS data has been providing new tools for the detection and typing of pathogens, including the investigation of the molecular basis of antibiotic resistance. This data availability opens up to new “in-silico” methods of analysis as functional genomics, GWAS, machine learning. These techniques have proven to be powerful methods to characterize bacterial features and to predict anti-microbial combinations. However, the performance of any in silico model depends on the quality of the input data. This Special Issue invites articles including, but not limited to, the following topics: functional genomics, methods and algorithms for genome analysis, including machine learning and systems biology approaches; characterization of mobile genetic elements, prophages, pseudogenes, and features conferring resistance to antimicrobials, disinfectants, biocides, chemical and physical stresses.

We are witnessing a dramatic increase in the number of bacterial pathogens resistant to multiple antibacterial agents. The availability of large collections of genome scale data, together with new data analysis methods, has proven to be a powerful method to predict anti-microbial combinations

Dr. Massimiliano Orsini
Dr. Carmen Losasso
Guest Editors

Manuscript Submission Information

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