AIMC Topic: Genome, Bacterial

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Whole-genome sequencing and comparative genomics reveal antimicrobial potential and adaptive traits of Bacillus velezensis AM12.

Functional & integrative genomics
The global rise of antimicrobial resistance has intensified the demand for novel antimicrobial agents with broad-spectrum efficacy and unique mechanisms of action. Herein, a marine-derived strain, Bacillus velezensis (B. velezensis) AM12, exhibiting ...

Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.

PLoS biology
Antimicrobial resistance (AMR) poses a growing threat to human health. Increasingly, genome sequencing is being applied for the surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR...

Unveiling the landscape of prokaryotic global regulators through deep protein language models.

mSystems
Global regulators (GRs) are key transcription factors that orchestrate the expression of multiple genes, playing essential roles in stress responses, virulence, secondary metabolism, and antibiotic resistance-traits that make them powerful tools for ...

Comparative assessment of annotation tools reveals critical antimicrobial resistance knowledge gaps in Klebsiella pneumoniae.

Scientific reports
Bacterial antimicrobial resistance (AMR) poses a significant public health threat. The increase of both global awareness and affordable whole genome sequencing has yielded an ever-growing collection of bacterial genome sequence datasets and correspon...

Global genomic survey of Kentucky: discovery of a chromosomeborne and the emergence of ST314, an MDR clone mediated by the IncR plasmid.

Emerging microbes & infections
Antimicrobial resistance (AMR) in enterica serotype Kentucky ( Kentucky) is a global challenge, with increasing resistance to cephalosporins, ciprofloxacin, and carbapenems significantly limiting treatment strategies, yet its worldwide dissemination...

Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.

BMC microbiology
Accurately identifying genes responsible for specific functions is a cornerstone of biological research, but current methods are often limited to single-species analyses. Here, we present a novel method, called Genomic and Phenotype-based machine lea...

HKDE-LACM: a hybrid model for lactic acid bacteria classification via k-mer and DNABERT-2 embedding fusion with cyclic DE-BO optimization.

BMC genomics
BACKGROUND: Lactic acid bacteria (LAB) play vital roles in food production and clinical applications. Accurate classification of LAB strains facilitates their functional development and targeted utilization. Although machine learning and deep learnin...

Prediction of reduction behavior by heating and strain variability of Campylobacter jejuni using amino acid phylogenetics from whole genome sequencing data.

International journal of food microbiology
Predicting bacterial behavior and strain variability is essential for quantitative microbial risk assessments in food safety. The growing availability of whole-genome sequencing (WGS) data enables deeper insights into microbial thermotolerance. Howev...

Investigating genetic, antigenic, and structural diversity in the outer membrane protein, PorB: implications for vaccine design.

mBio
UNLABELLED: Vaccines targeting are needed to reduce disease burden and help address the problem of antimicrobial resistance, with an understanding of relationships between gonococcal genetics and molecules influencing diversity, infection, and the i...

Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens.

Communications biology
Variations in host species significantly impact bacterial growth traits and antibiotic resistance, making it essential to consider host origin when evaluating the zoonotic potential of pathogens. This study focuses on multiple Brucella species, which...