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Related Concept Videos

DNA-only Transposons02:57

DNA-only Transposons

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DNA-only transposons are called autonomous transposons since they code for the enzyme transposase that is required for the transposition mechanism. Insertion of transposons can alter gene functions in multiple ways. They can mutate the gene, alter gene expression by introducing a novel promoter or insulator sequence, introduce new splice sites, and change the mRNA transcripts produced, or remodel chromatin structure.
The donor site from where the transposon is excised is either degraded or...
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Transposons01:24

Transposons

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Transposons, or "jumping genes," are small mobile genetic elements (MGEs) that range from 700 to 40,000 base pairs in length. They are found in all organisms and can move within the same chromosome or transfer to different chromosomes. In some cases, transposons can also jump between different host DNA molecules, such as plasmids or viruses, contributing to genetic variability.Barbara McClintock first discovered these mobile genetic elements in the 1940s while studying maize genetics, and she...
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Overview of Transposition and Recombination02:13

Overview of Transposition and Recombination

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Transposons make up a significant part of genomes of various organisms. Therefore, it is believed that transposition played a major evolutionary role in speciation by changing genome sizes and modifying gene expression patterns. For example, in bacteria, transposition can lead to conferring antibiotic resistance. Movement of transposable elements within the genetic pool of pathogenic bacteria can aid in transfer of antibiotic-resistant genetic elements. In eukaryotes, transposons can carry out...
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Non-LTR Retrotransposons03:18

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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Related Experiment Video

Updated: Oct 1, 2025

Creation of a Dense Transposon Insertion Library Using Bacterial Conjugation in Enterobacterial Strains Such As Escherichia Coli or Shigella flexneri
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Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.

Vishal Sarsani1, Berent Aldikacti2, Shai He1

  • 1Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.

Plos Computational Biology
|March 7, 2022
PubMed
Summary

This study introduces a new statistical model for transposon sequencing data to identify bacterial genes essential under specific environmental conditions. The method enhances understanding of gene function and bacterial adaptation to stress.

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Deep sequencing of microbial genomes advances bacterial gene function understanding.
  • Transposon insertion sequencing (Tn-seq) combines sequencing with mutagenesis to explore gene essentiality across environments.

Purpose of the Study:

  • To develop a model-based statistical method for analyzing Tn-seq data.
  • To estimate changes in transposon insertions due to gene-environment interactions without data transformations.

Main Methods:

  • Utilized regularized negative binomial regression for estimating transposon insertion changes.
  • Employed an empirical Bayes model to calculate local false discovery rates.
  • Applied the model to RB-TnSeq and Tn-seq data from Caulobacter crescentus.

Main Results:

  • Identified conditionally beneficial and detrimental genes under various stress conditions.
  • The model effectively analyzed both unique and total transposon count data.
  • Provided insights into gene functions and roles during environmental stress.

Conclusions:

  • The proposed model accurately identifies genes with significant changes in transposon counts.
  • This approach enhances the study of bacterial gene essentiality and adaptation.
  • Offers a robust method for genetic interaction studies using Tn-seq data.