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Related Experiment Videos

Detection of transposable elements by their compositional bias.

Olivier Andrieu1, Anna-Sophie Fiston, Dominique Anxolabéhère

  • 1Laboratoire Dynamique du Génome et Evolution, Institut Jacques Monod, Tour 42-32, 5 place Jussieu, 75251 Paris, France. andrieu@ijm.jussieu.fr

BMC Bioinformatics
|July 15, 2004
PubMed
Summary

Transposable elements (TEs) have distinct nucleotide compositions compared to host genes. Hidden Markov Models (HMMs) leverage these differences for accurate TE detection and analysis in genomic sequences.

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TE Hub: A community-oriented space for sharing and connecting tools, data, resources, and methods for transposable element annotation.

Mobile DNA·2021

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Transposable elements (TEs) are mobile genetic sequences found in most genomes.
  • TEs exhibit distinct nucleotide compositions (codon usage, dinucleotide frequencies) compared to host genes.
  • These compositional differences offer a potential avenue for TE identification and study.

Purpose of the Study:

  • To investigate the utility of nucleotide compositional differences for detecting and analyzing transposable element (TE) sequences.
  • To develop and apply probabilistic models for distinguishing TE sequences from host genes.

Main Methods:

  • Utilized hidden Markov models (HMMs) to analyze nucleotide sequence composition.
  • Incorporated base composition (1-4 nucleotide words) and sequence heterogeneity (coding vs. non-coding regions).

Related Experiment Videos

  • Analyzed sequences from Class I TEs, Class II TEs, and genes in Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana.

    • Main Results:

    • TE and host gene sequences demonstrated distinct and homogeneous compositions, allowing differentiation between TE classes and genes.
    • The specific nucleotide composition of TEs varied across the three studied species.
    • HMMs successfully distinguished between TE classes and host genes based on sequence composition.

    Conclusions:

    • The HMM-based approach effectively detects and annotates TEs in genomic sequences, complementing homology-based methods.
    • This method aids in identifying TE-like coding regions within genomic sequences, even ancient or divergent ones.
    • The findings facilitate detailed annotation of transposable elements, including potentially non-functional or degraded coding regions.