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Related Concept Videos

Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
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Robbers Cave04:49

Robbers Cave

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Abdominal Regions and Quadrants01:19

Abdominal Regions and Quadrants

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Radical Formation: Overview01:03

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Review and Preview01:10

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Coral Reef Arks: An In Situ Mesocosm and Toolkit for Assembling Reef Communities
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An insight into iTRAQ: where do we stand now?

Caroline Evans1, Josselin Noirel, Saw Yen Ow

  • 1The ChELSI Institute, Chemical and Biological Engineering, The University of Sheffield, Sheffield, UK.

Analytical and Bioanalytical Chemistry
|March 28, 2012
PubMed
Summary
This summary is machine-generated.

Isobaric tags for relative and absolute quantification (iTRAQ) may not be truly quantitative, often underestimating abundance changes. This review examines iTRAQ

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Area of Science:

  • Proteomics
  • Quantitative Mass Spectrometry

Background:

  • Isobaric tags for relative and absolute quantification (iTRAQ) is a popular technique for relative protein quantification.
  • Its application is widespread in various proteomic workflows.
  • Comparison with other quantitative methods like SILAC, label-free, and SRM is crucial.

Purpose of the Study:

  • To review the iTRAQ literature, focusing on its usage and limitations.
  • To compare iTRAQ with other quantitative proteomics techniques.
  • To identify technical issues affecting iTRAQ accuracy and precision.

Main Methods:

  • Literature review of iTRAQ applications and comparisons.
  • Analysis of technical and analytical limitations of iTRAQ.
  • Discussion of advancements in iTRAQ methodology.

Main Results:

  • iTRAQ may not be truly quantitative, potentially underestimating abundance changes.
  • Technical and analytical limitations undermine iTRAQ's reliability.
  • iTRAQ is losing ground to label-free quantification methods.

Conclusions:

  • iTRAQ has inherent weaknesses affecting its quantitative accuracy.
  • Technical improvements are being explored to enhance iTRAQ performance.
  • Label-free methods are emerging as a preferred alternative for relative quantification.