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Related Concept Videos

Horizontal Gene Transfer01:27

Horizontal Gene Transfer

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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:...
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Types of Genetic Transfer Between Organisms02:18

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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.
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Types of Genetic Transfer Between Organisms02:18

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Transduction01:16

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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...
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Conjugation01:19

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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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Genomic DNA in Prokaryotes00:46

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The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
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Although bacterial genomes are much...
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Detection of Horizontal Gene Transfer Mediated by Natural Conjugative Plasmids in E. coli
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Genomic data representations for horizontal gene transfer detection.

Andre Jatmiko Wijaya1,2, Aleksandar Anžel1, Hugues Richard2,3

  • 1Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany.

NAR Genomics and Bioinformatics
|December 12, 2025
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Summary
This summary is machine-generated.

Machine learning models can detect horizontal gene transfer (HGT) of antimicrobial resistance (AMR) genes more effectively. Optimal genomic data representations, like RCKmer, significantly improve model performance for identifying superbugs.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Horizontal gene transfer (HGT) accelerates the spread of antimicrobial resistance (AMR) via mobile genetic elements, driving the evolution of multidrug-resistant pathogens.
  • Traditional methods for detecting HGT lack resolution for complex transfer events, hindering efforts to mitigate AMR.
  • Machine learning (ML) shows promise for improved HGT detection, but its performance is highly dependent on the chosen genomic data representation.

Purpose of the Study:

  • To evaluate the impact of different genomic data representations on ML model performance for HGT detection.
  • To identify the optimal data representation and ML model combination for accurately detecting HGT events, particularly those involving AMR genes.
  • To provide state-of-the-art models for genomic surveillance and the identification of AMR gene transfer.

Main Methods:

  • Evaluated 44 distinct genomic data representations using five different ML models.
  • Tested model performance across four diverse datasets to assess generalization capabilities.
  • Utilized metrics such as F1 score and Matthews Correlation Coefficient (MCC) to quantify performance.

Main Results:

  • ML model performance in HGT detection is strongly influenced by the genomic data representation used.
  • The RCKmer-based representation (k=7) combined with a support vector machine achieved optimal performance (F1: 0.959; MCC: 0.908).
  • Models trained on multi-species datasets demonstrated superior generalization, highlighting the benefit of diverse training data.

Conclusions:

  • Task-specific genomic data representations are crucial for enhancing ML-based genomic surveillance.
  • The RCKmer representation offers a highly effective method for detecting HGT events, aiding in the identification of AMR gene transfer.
  • This research provides optimized models for identifying genomic islands and understanding AMR gene flow between species.