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Related Concept Videos

Classifying Matter by Composition03:35

Classifying Matter by Composition

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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. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
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Elements and Compounds01:27

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Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond.
Elements
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Classification of Elements and Compounds02:54

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Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Mean Absolute Deviation01:13

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
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Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
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DeviaTE: Assembly-free analysis and visualization of mobile genetic element composition.

Lukas Weilguny1, Robert Kofler1

  • 1Institut für Populationsgenetik, Vetmeduni Vienna, Wien, Austria.

Molecular Ecology Resources
|May 7, 2019
PubMed
Summary
This summary is machine-generated.

DeviaTE is a new tool that accurately measures transposable element (TE) abundance in genomes. It provides unbiased estimates by accounting for deletions, crucial for understanding TE evolution and invasions.

Keywords:
pythonassembly freedata visualizationdivergencemobile genetic elementtransposon

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Transposable elements (TEs) are mobile DNA sequences found in most genomes, with varying abundance and diversity.
  • Understanding TE composition is vital for evolutionary dynamics, population genetics, and identifying TE invasions.
  • Previous methods often underestimate TE abundance due to factors like internal deletions.

Purpose of the Study:

  • To develop and validate DeviaTE, a novel computational tool for analyzing and visualizing TE abundance and diversity.
  • To provide unbiased estimates of TE abundance by correcting for sequence variations like deletions.
  • To enable comparative analyses of TE composition across different samples, populations, and species.

Main Methods:

  • DeviaTE analyzes sequencing reads (Illumina or Sanger) against TE consensus sequences.
  • It assesses TE coverage, sequence divergence, SNPs, indels, and internal/terminal deletions.
  • Unbiased TE abundance is estimated by comparing TE coverage to single-copy genes.

Main Results:

  • DeviaTE generates tables and visualizations of TE composition, including coverage and sequence variation.
  • The tool accurately estimates TE abundance, outperforming naive methods that ignore deletions.
  • DeviaTE successfully identified clinal variation, compared TE diversity across species, and monitored TE invasions using published data.

Conclusions:

  • DeviaTE offers a robust and unbiased method for quantifying transposable element abundance and diversity.
  • The tool is valuable for studying TE dynamics, evolutionary implications, and invasion events in various species.
  • DeviaTE is implemented in Python and publicly available for genomic research.