AMRomics: a scalable workflow to analyze large microbial genome collections.

Area of Science:

• Microbial genomics
• Bioinformatics
• Antimicrobial resistance surveillance

Background:

• Whole genome analysis is crucial for monitoring antimicrobial resistance (AMR) strains.
• Rapidly growing microbial sequencing data requires efficient computational pipelines.
• Existing genomic surveillance methods struggle with scalability and turnaround time for large bacterial populations.

Purpose of the Study:

• To introduce AMRomics, an optimized computational pipeline for microbial genomics.
• To address the need for fast and scalable analysis of large microbial datasets.
• To improve the efficiency of genomic surveillance for antimicrobial resistance.

Main Methods:

• Development of the AMRomics pipeline for efficient big data processing.
• Comparative analysis of AMRomics against existing tools using diverse bacterial datasets.
• Evaluation of pipeline performance in terms of speed and scalability.

Main Results:

• AMRomics demonstrates efficient performance with large microbial genomic datasets.
• The pipeline generates results comparable to established methods.
• AMRomics offers significant performance improvements over competitive tools.

Conclusions:

• AMRomics provides an optimized solution for microbial genomics and AMR surveillance.
• The pipeline is suitable for handling large-scale datasets efficiently.
• Open-source availability facilitates wider adoption and research.