AIMC Topic: Genome, Bacterial
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Using core genome and machine learning for serovar prediction in Salmonella enterica subspecies I strains.
FEMS microbiology letters
Jan 10, 2025
This study presents a dual investigation of Salmonella enterica subspecies I, focusing on serovar prediction and core genome characteristics. We utilized two large genomic datasets (panX and NCBI Pathogen Detection) to test machine learning methods f...
PanKB: An interactive microbial pangenome knowledgebase for research, biotechnological innovation, and knowledge mining.
Nucleic acids research
Jan 6, 2025
The exponential growth of microbial genome data presents unprecedented opportunities for unlocking the potential of microorganisms. The burgeoning field of pangenomics offers a framework for extracting insights from this big biological data. Recent a...
Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bact...
Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.
Nucleic acids research
Nov 27, 2024
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...
A deep learning method to predict bacterial ADP-ribosyltransferase toxins.
Bioinformatics (Oxford, England)
Jul 1, 2024
MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent v...
Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.
Bioinformatics (Oxford, England)
Jun 28, 2024
MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuber...
Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants.
GigaScience
Jan 2, 2024
BACKGROUND: Phage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations prese...
NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.
Briefings in bioinformatics
Nov 22, 2023
Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequ...
Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes.
Nucleic acids research
Feb 22, 2022
Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequenc...
Machine learning for identifying resistance features of using whole-genome sequence single nucleotide polymorphisms.
Journal of medical microbiology
Nov 1, 2021
, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of is increasing year by year, posing a severe threat to public health worldwide. has been listed as one of the pathogens causing the global c...
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