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LTR Retrotransposons03:08

LTR Retrotransposons

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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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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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Classifying Matter by Composition03:35

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
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Termination of Translation01:44

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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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A machine learning based framework to identify and classify long terminal repeat retrotransposons.

Leander Schietgat1, Celine Vens1,2,3, Ricardo Cerri4

  • 1Department of Computer Science, KU Leuven, Leuven, Belgium.

Plos Computational Biology
|April 24, 2018
PubMed
Summary
This summary is machine-generated.

TE-Learner, a machine learning framework, accurately identifies and classifies transposable elements (TEs) in genomes. This novel approach outperforms existing methods for LTR retrotransposon detection, enhancing genome analysis and evolutionary studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transposable elements (TEs) comprise a significant portion of eukaryotic genomes and play roles in genome size variation and genetic diversity.
  • Accurate identification and classification of TEs are crucial for understanding their impact on gene function and genome evolution.

Purpose of the Study:

  • To introduce TE-Learner, a machine learning-based framework for automated TE identification and classification.
  • To implement and evaluate TE-Learner for identifying LTR retrotransposons, a specific class of TEs.

Main Methods:

  • Developed a machine learning framework (TE-Learner) for automated TE identification and classification.
  • Implemented TE-Learner for LTR retrotransposons, focusing on genomes of Drosophila melanogaster and Arabidopsis thaliana.
  • Compared TE-Learner's performance against RepeatMasker, Censor, and LtrDigest for TE identification and classification.

Main Results:

  • TE-Learner demonstrated superior predictive performance compared to existing methods.
  • The framework efficiently learns models and makes predictions for TEs.
  • TE-Learner successfully identified TEs missed by other methods, with many predictions showing homology to known TEs.

Conclusions:

  • TE-Learner offers a novel and effective machine learning approach for TE identification and classification.
  • This method advances the study of TEs, their role in genome evolution, and genetic diversity.
  • TE-Learner provides a powerful tool for genomic research, improving upon current TE detection capabilities.