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Related Concept Videos

Horizontal Gene Transfer01:27

Horizontal Gene Transfer

Horizontal gene transfer (HGT) is a process where genetic material moves between organisms within the same generation, unlike vertical gene transfer, which occurs from parent to offspring. HGT plays a crucial role in microbial evolution, adaptation, and survival, particularly in shared environments like the human gut.Mobile genetic elements such as plasmids, prophages, integrons, insertion sequences, and transposons facilitate this process. HGT occurs through three primary mechanisms:...
Types of Genetic Transfer Between Organisms02:18

Types of Genetic Transfer Between Organisms

Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
Types of Genetic Transfer Between Organisms02:18

Types of Genetic Transfer Between Organisms

Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
Transduction01:16

Transduction

Among the three main modes of HGT—transformation, conjugation, and transduction—transduction is unique in that it is mediated by bacteriophages, or bacterial viruses.Transduction occurs in two ways. Generalized transduction occurs during the lytic cycle of a bacteriophage infection. In this process, bacteriophages infect bacterial cells, replicate within them, and ultimately cause cell lysis, releasing newly assembled virions. Occasionally, random fragments of the bacterial genome are...
Evolution of Microbial Genome01:08

Evolution of Microbial Genome

Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
Conjugation01:19

Conjugation

Conjugation is a form of horizontal gene transfer that primarily occurs in bacteria and some archaea, promoting genetic diversity and adaptation. Bacteria can acquire resistance genes through conjugative plasmids, allowing them to survive antibiotic treatments that would otherwise be lethal. This process involves direct contact between cells through specialized structures such as the sex pilus and is mediated by conjugative plasmids, including the F (fertility) factor.Conjugation requires...

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Updated: May 31, 2026

Detection of Horizontal Gene Transfer Mediated by Natural Conjugative Plasmids in E. coli
06:56

Detection of Horizontal Gene Transfer Mediated by Natural Conjugative Plasmids in E. coli

Published on: March 24, 2023

Evaluating ensemble learning approaches for horizontal gene transfer detection.

Andre Jatmiko Wijaya1,2, Aleksandar Anžel1, Georges Hattab3,4

  • 1Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch Institute, 13353, Berlin, Germany.

Scientific Reports
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

Integrating diverse data representations in ensemble learning for genomic island (GI) detection showed minor recall improvements for classification but did not enhance GI localization. This suggests limitations in cross-task generalizability for current methods.

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Last Updated: May 31, 2026

Detection of Horizontal Gene Transfer Mediated by Natural Conjugative Plasmids in E. coli
06:56

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Published on: March 24, 2023

Methodology for the Study of Horizontal Gene Transfer in Staphylococcus aureus
10:39

Methodology for the Study of Horizontal Gene Transfer in Staphylococcus aureus

Published on: March 10, 2017

Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation
10:41

Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation

Published on: January 4, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Horizontal gene transfer (HGT) drives antimicrobial resistance (AMR) spread via genomic islands (GIs).
  • Accurate GI detection is crucial for AMR surveillance.
  • Machine learning (ML) shows promise for GI detection, with performance linked to data representations.

Purpose of the Study:

  • To evaluate ensemble learning integrating diverse data representations for GI detection.
  • To assess the efficacy of this approach for both GI classification and localization.
  • To determine the optimal combination of data representations for GI detection.

Main Methods:

  • Implemented a two-stage ensemble selection strategy.
  • Combined multiple, low-correlated data representations.
  • Evaluated ensemble performance on GI classification and localization tasks.

Main Results:

  • Ensemble classifiers showed a slight, statistically insignificant increase in Recall for GI classification compared to individual representations.
  • The ensemble approach did not improve the localization of GIs within genomic sequences.
  • Cross-task generalizability of the ensemble method was constrained.

Conclusions:

  • Combining diverse data representations in ensemble learning offers limited benefits for GI detection, particularly for localization.
  • Current methods face challenges in generalizability across different genomic tasks.
  • Future research should explore novel problem formulations for improved GI detection.