AIMC Topic: Genome, Bacterial

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Cyanobacteria Join the Kahalalide Conversation: Genome and Metabolite Evidence for Structurally Related Peptides.

Journal of the American Chemical Society
Kahalalide F is a cyclic depsipeptide with notable anticancer properties, initially discovered from the green alga sp. and its molluscan predator . Recent studies have pinpointed a bacterial endosymbiont of the green alga, Endobryopsis kahalalidefa...

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.

Microbiology spectrum
Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) to guide timely treatments. Traditional culture-based AST methods are slow, while existing whole-gen...

Genomic diversity of Cronobacter sakazakii across the food system to consumers at the global scale.

International journal of food microbiology
Understanding how foodborne pathogens adapt to changing environments is essential for improving food safety monitoring and control. Cronobacter sakazakii, a persistent opportunistic pathogen associated with powdered infant formula outbreaks, poses cr...

Modeling host-pathway dynamics at the genome scale with machine learning.

Metabolic engineering
Pathway engineering offers a promising avenue for sustainable chemical production. The design of efficient production systems requires understanding complex host-pathway interactions that shape the metabolic phenotype. While genome-scale metabolic mo...

Pato: prediction of probiotic bacteria using metabolic features.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
Probiotics have gained recognition for their health-promoting benefits, particularly in the gastrointestinal and immunological systems. Among promising probiotic candidates, Lactobacillus strains, belonging to the lactic acid bacteria (LAB) group, pl...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Journal of microbiological methods
Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential genes in pathogens and non-pathogenic genes. Since, identification of therapeutic targets and understand...

Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group A .

Journal of microbiology and biotechnology
Group A (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these finding...

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.

Scientific reports
Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, an...

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial is...

deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.

mSystems
Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec) in the active sites, which is encoded by an in-frame UGA codon. Compared to eukaryotes, identificat...