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Related Concept Videos

DNA-only Transposons02:57

DNA-only Transposons

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...
Transposons01:24

Transposons

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...
Overview of Transposition and Recombination02:13

Overview of Transposition and Recombination

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...
LTR Retrotransposons03:08

LTR Retrotransposons

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.
The internal coding region of LTR retrotransposons and their mechanism of transposition closely resembles a...

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Related Experiment Video

Updated: Jun 16, 2026

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
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Published on: August 12, 2019

Transposon identification using profile HMMs.

Paul T Edlefsen1, Jun S Liu

  • 1Department of Statistics, Harvard University, Cambridge, MA, USA. edlefsen@stat.harvard.edu

BMC Genomics
|February 18, 2010
PubMed
Summary
This summary is machine-generated.

New algorithms improve profile hidden Markov models (profile HMMs) for modeling transposon DNA sequences. These methods offer better parameter estimation, enhancing our ability to study jumping genes and their role in genomics.

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Published on: December 18, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transposons, or "jumping genes," constitute significant repetitive genomic content and influence transcriptional regulation, implicated in human diseases.
  • Transposon-derived sequences are linked to microRNAs, viruses, genes, pseudogenes, and gene promoters, posing modeling challenges due to poor conservation.
  • Profile hidden Markov models (profile HMMs), typically used for protein families, are underutilized for DNA families, with the standard Baum-Welch algorithm facing convergence issues in the DNA domain.

Purpose of the Study:

  • To evaluate novel algorithms for improving profile HMM parameter estimation in DNA sequence modeling.
  • To address the limitations of the Baum-Welch algorithm in modeling poorly conserved DNA families like transposons.

Main Methods:

  • Conditional Baum-Welch algorithm
  • Dynamic Model Surgery algorithm
  • Simulation studies and application to MIR transposon family modeling

Main Results:

  • Conditional Baum-Welch and Dynamic Model Surgery demonstrated superior parameter estimation for profile HMMs compared to standard methods.
  • Improved performance was observed across a range of conditions in both simulation studies and real data application.

Conclusions:

  • The new algorithms broaden the applicability of profile HMMs for diverse DNA sequence family modeling tasks.
  • These advancements facilitate the search for and modeling of virus-like transposons prevalent in all known genomes.