Antimicrobial resistence prediction using metagenomic sequencing

What is it about?

This was a project involving metagenomic sequencing to antimicrobial resistance prediction. We proposed a computational pipeline for the AMR evaluation of a microbiome sample based on high-throughput sequencing data. MGS2AMR detects ARG and suggests their origin. We demonstrate an application of the pipeline by using ML to detect the presence of potential pathogens within the intestinal microbiome and evaluating their geno and phenotypical AMR. This can provide the clinician with advance notice of the presence and antibiotic resistance profile of an organism causing impending invasive infection. This overall workflow can improve clinical practice by providing the AMR information early in the infection phase before complications, such as sepsis, arise.

Featured Image

Photo by National Cancer Institute on Unsplash

Photo by National Cancer Institute on Unsplash

Why is it important?

Antibiotic-resistant bacteria are a global problem causing millions of deaths worldwide. So early detection of emerging human pathogens and rapid evaluation of their antimicrobial resistance (AMR) profile are of vital importance.