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Related Concept Videos

Transposons01:24

Transposons

183
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...
183
DNA-only Transposons02:57

DNA-only Transposons

14.8K
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...
14.8K
Overview of Transposition and Recombination02:13

Overview of Transposition and Recombination

16.1K
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...
16.1K

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Related Experiment Video

Updated: Sep 19, 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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Creation of a Dense Transposon Insertion Library Using Bacterial Conjugation in Enterobacterial Strains Such As Escherichia Coli or Shigella flexneri

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The Mycobacterium tuberculosis Transposon Sequencing Database (MtbTnDB): A Large-Scale Guide to Genetic Conditional

Adrian Jinich1,2, Anisha Zaveri3, Michael A DeJesus4

  • 1Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA.

Molecular Microbiology
|June 17, 2025
PubMed
Summary

A new database, MtbTnDB, standardizes and centralizes Mycobacterium tuberculosis transposon sequencing (TnSeq) data. This resource facilitates comparative analysis of genetic essentiality, accelerating gene function discovery and prediction in this pathogen.

Keywords:
databasefunctional geneticsmicrobiologytransposon sequencingtuberculosis

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Area of Science:

  • Microbiology
  • Genomics
  • Systems Biology

Background:

  • Understanding gene function is crucial for bacterial biology, particularly for pathogens like Mycobacterium tuberculosis (Mtb).
  • Transposon sequencing (TnSeq) is a key technique for genome-wide essentiality profiling in bacteria.
  • Existing Mtb TnSeq data is fragmented, hindering comprehensive analysis and consistent understanding of conditional genetic essentiality.

Purpose of the Study:

  • To create a centralized, standardized repository for publicly available Mtb TnSeq screens.
  • To develop an interactive web application for data access, visualization, and functional predictions.
  • To facilitate comparative analyses of TnSeq data across diverse conditions.

Main Methods:

  • Collation and standardization of approximately 150 Mtb TnSeq screens.
  • Development of the Mtb transposon sequencing database (MtbTnDB) and an associated interactive web app.
  • Statistical analyses including gene neighborhood profiling, functional enrichment analysis, and machine learning model evaluation.

Main Results:

  • MtbTnDB provides open access to standardized Mtb TnSeq data and visualizations.
  • Genes in close genomic proximity exhibit similar TnSeq profiles.
  • Clusters of genes with similar TnSeq profiles are enriched for shared functional categories.
  • Machine learning models trained on TnSeq data show promise for predicting functions of unannotated genes.

Conclusions:

  • MtbTnDB significantly enhances the accessibility and utility of Mtb TnSeq data.
  • The database accelerates the exploration of conditional genetic essentiality and gene function in Mtb.
  • MtbTnDB aids in understanding the functional organization of the Mtb genome and predicting gene function.