Decoding the Antibiotic Resistance Puzzle: Unleashing the Power of Computational Analysis and Dynamic Tracking in Microbial (Meta)Genomics and Phenomics

Antibiotic resistance poses a significant global health challenge, necessitating a multidisciplinary effort to decipher the mechanisms underlying resistance and develop effective strategies combat resistance. In recent years, the emergence of high-throughput sequencing technologies and other omics technologies combined with computational analysis tools has revolutionized our ability to study microbial (meta)genomic and phenomic data, offering new opportunities to explore and unravelling the complexity of antibiotic resistance, paving the way for new strategies, diagnostics, and therapeutic interventions in the fight against resistant infections.



This Research Topic aims to bring together researchers and experts to unravel the complexities of antibiotic resistance by harnessing the power of computational analysis and dynamic tracking in microbial (meta)genomics and/or phenomics. We invite submissions that highlight innovative approaches, novel algorithms, and data-driven strategies to understand the molecular basis of antibiotic resistance, identify resistance genes, track resistance evolution, and predict resistance patterns at both the organismal and community levels. By utilizing the power of computational tools, this topic aims to unravel the complexity of antibiotic resistance, paving the way for new strategies, diagnostics, and therapeutic interventions in the fight against resistant infections.



We welcome a variety of contributions, including original research, reviews, novel methodologies, hypotheses/theories, perspectives, and both experimental and in silico/computational studies. The themes include, but are not limited to:



• Computational tools and algorithms enabling the identification and annotation of antibiotic resistance genes in microbial genomes and metagenomic data.

• Modelling of antibiotic resistance evolution and prediction of resistance patterns using population genomics and evolutionary dynamics.

• Network analysis and systems biology approaches to decipher the regulatory networks and signalling pathways governing antibiotic resistance.

• Unravelling the role of mobile genetic elements, plasmids, and horizontal gene transfer in antibiotic resistance dissemination.

• Application of machine learning, artificial intelligence, and data mining techniques to analyse large-scale (meta)genomic and phenomic datasets for antibiotic resistance surveillance and prediction.

• Innovative approaches for tracking and characterizing antibiotic resistance at the microbial community and microbiome levels.

• Exploring the relationship between antibiotic resistance and microbial ecology, host-pathogen interactions, and intra-species communication.

• Meta-analyses and comparative genomics studies uncovering patterns of antibiotic resistance across diverse environments and patient cohorts.

• Translational implications and potential interventions arising from computational analysis and dynamic tracking of antibiotic resistance.