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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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Transposons01:24

Transposons

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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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Overview of Transposition and Recombination02:13

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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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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.
The internal coding region of LTR retrotransposons and their mechanism of transposition closely resembles a...
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Translocation of Proteins into the Mitochondria01:19

Translocation of Proteins into the Mitochondria

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Mitochondrial precursors are translocated to the internal subcompartments via independent mechanisms involving distinct protein machineries called translocases.
Sorting of outer membrane proteins:
Mitochondrial outer membrane proteins are of two types: the transmembrane, beta-barrel porins, and the membrane-anchored, alpha-helical proteins. Beta-barrel porin precursors are translocated by the TOM complex and inserted into the outer mitochondrial membrane by the SAM complex. In contrast,...
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Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

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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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Testing the mean matrix in high-dimensional transposable data.

Anestis Touloumis1, Simon Tavaré1, John C Marioni2

  • 1Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, U.K.

Biometrics
|January 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new nonparametric testing method for analyzing complex, high-dimensional data matrices. The procedure effectively tests for structural hypotheses in data, even with dependent variables, offering robust performance.

Keywords:
High-dimensional transposable dataHypothesis testingMean matrixNonparametric test

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Area of Science:

  • Statistics
  • Bioinformatics
  • Data Science

Background:

  • High-dimensional data analysis presents challenges due to complex dependencies.
  • Testing structural hypotheses in mean matrices requires methods that account for row/column variable interdependencies.

Purpose of the Study:

  • To develop a computationally inexpensive, nonparametric testing procedure for assessing structural hypotheses in high-dimensional data.
  • To evaluate the performance of the proposed method in preserving nominal size and maintaining power, especially with dependent variables.

Main Methods:

  • A generic nonparametric testing procedure was developed to assess the constancy of mean vectors within predefined subsets of columns (or rows).
  • The method accounts for dependence structures among and/or between row and column variables.

Main Results:

  • Simulation studies demonstrated good performance of the proposed testing procedure.
  • The method successfully preserves nominal size and remains powerful, outperforming simpler practical approaches when variables are not independent.

Conclusions:

  • The developed nonparametric testing procedure offers a reliable and efficient tool for hypothesis testing in high-dimensional transposable data.
  • The methodology is applicable to complex biological datasets, as illustrated by gene expression microarray examples.