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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.
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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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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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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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LTR retrotransposons are class I transposable elements with long terminal repeats flanking an internal coding region. These elements are less abundant in mammals compared to other class I transposable elements. About 8 percent of human genomic DNA comprises LTR retrotransposons. Some of the common examples of LTR retrotransposons are Ty elements in yeast and Copia elements in Drosophila.
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A benchmark of transposon insertion detection tools using real data.

Pol Vendrell-Mir1, Fabio Barteri1, Miriam Merenciano2

  • 11Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Bellaterra, 08193 Barcelona, Spain.

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Summary
This summary is machine-generated.

Identifying transposable element (TE) insertions is crucial for understanding genotype-phenotype links. This study benchmarks TE detection tools using a curated dataset, revealing overestimated sensitivity with simulated data and highlighting tool performance variations across TE types.

Keywords:
BenchmarkPolymorphismResequencingTransposable elementsTransposon insertion

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Transposable elements (TEs) drive genomic variability and impact phenotype, making their accurate identification essential.
  • Current genotype-phenotype studies often overlook TEs, focusing on single nucleotide polymorphisms due to easier detection with short-read sequencing.
  • Existing bioinformatic tools for transposon insertion detection are typically validated using simulated data, which may not reflect real-world complexities.

Purpose of the Study:

  • To evaluate the performance of commonly used bioinformatic tools for identifying transposable element insertions.
  • To address the limitations of using simulated data for tool validation by creating a dataset of manually curated, validated insertions.
  • To provide insights into the sensitivity and precision of different tools for detecting various types of TEs.

Main Methods:

  • A dataset of validated transposable element insertions (LTR-retrotransposons and MITEs) was constructed by comparing and manually curating two high-quality rice genomes.
  • Twelve commonly used bioinformatic tools for transposon insertion detection were benchmarked using this curated dataset.
  • The performance of these tools was further assessed using experimentally validated insertion datasets from *Drosophila* and humans.

Main Results:

  • The sensitivity of most tested tools was overestimated when using simulated data compared to the curated dataset.
  • Increased sequencing coverage generally improved sensitivity but reduced precision in TE detection.
  • Significant variations in tool performance were observed, with some tools excelling at detecting specific TE types (e.g., LTR-retrotransposons vs. MITEs).
  • The observed trends in tool performance were consistent across different species with varying genome sizes and complexities.

Conclusions:

  • The study highlights the need for careful tool selection based on research objectives when identifying transposable element insertions.
  • Combining multiple bioinformatic tools can potentially enhance detection sensitivity while maintaining acceptable precision.
  • This work provides a valuable resource and benchmark for future studies on transposable element dynamics and their impact on genomes.