Antibiotic Resistome: Improving Detection and Quantification Accuracy for Comparative Metagenomics.

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Antibiotic resistance (AR) is a growing global health threat.
• Advances in DNA sequencing necessitate robust methods for analyzing AR genes in metagenomic data.
• Accurate quantification of AR genes is vital for understanding the antibiotic resistome.

Purpose of the Study:

• To optimize and enhance existing methodologies for the in silico detection and quantification of AR genes in metagenomic samples.
• To incorporate the impact of AR-generating mutations in antibiotic target genes into detection methods.
• To improve the accuracy of assessing the antibiotic resistome.

Main Methods:

• Comparative metagenomic analysis of publicly available datasets.
• Development and application of improved AR gene detection algorithms.
• Metagenome simulation experiments to investigate factors influencing AR gene quantification.

Main Results:

• Existing methods may falsely assign or neglect mutation-generated AR genes, altering resistome assessment.
• Key factors influencing AR gene quantification include genome size, AR gene length, sequencing depth, and platform.
• The proposed improved methodologies demonstrate reliable results on both real and simulated metagenomic data.

Conclusions:

• Optimized methodologies provide more accurate detection and quantification of AR genes and the antibiotic resistome.
• Accounting for AR-generating mutations is essential for precise resistome analysis.
• The improved methods offer a reliable approach for assessing AR potential in diverse metagenomic samples.