AIMC Topic: Genome, Bacterial
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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...
Explainability in transformer models for functional genomics.
Briefings in bioinformatics
Sep 2, 2021
The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs fr...
CyanoPATH: a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms.
Briefings in bioinformatics
Jul 20, 2021
CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters. Based on the literature of empirical studies and genome/protein databases, it summarize...
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