ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

ABSTRACT:

Background

Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale

SUBMITTER: Pei Y 

PROVIDER: S-EPMC11080312 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature