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Related Concept Videos

Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Review and Preview01:10

Review and Preview

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.
Percentiles are a type of fractile that partition data into...
z Scores and Unusual Values01:07

z Scores and Unusual Values

The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...

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D-score: a search engine independent MD-score.

Marc Vaudel1, Daniela Breiter, Florian Beck

  • 1Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.

Proteomics
|January 12, 2013
PubMed
Summary

This study introduces a new D-score to improve the accuracy of pinpointing post-translational modifications (PTMs) on peptides identified by mass spectrometry. The D-score enhances PTM localization, increasing correctly identified phosphorylation sites by up to 25.7%.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Identifying post-translational modifications (PTMs) on peptides is crucial in proteomics.
  • Accurate localization of PTMs to specific sites on peptide sequences remains a significant challenge in gel-free mass spectrometry.
  • Current methods often rely on secondary peptide match scores, which can be unreliable for site inference.

Purpose of the Study:

  • To develop a novel scoring method, the D-score, for improving PTM site localization accuracy.
  • To evaluate the D-score's performance across multiple search engines (Mascot, OMSSA, X!Tandem).
  • To demonstrate the score's applicability to various modifications without additional experimental costs.

Main Methods:

  • Estimation of posterior error probabilities for peptide candidates.
  • Development and application of the D-score for PTM localization.
  • Evaluation using a high-resolution dataset of synthetic phosphopeptides.

Main Results:

  • The D-score effectively estimates PTM localization quality across multiple search engines.
  • For peptides with uncertain phosphorylation site inference, the D-score increased correctly localized phosphorylation sites by up to 25.7% compared to Mascot alone.
  • Performance gains varied depending on the fragmentation method used.

Conclusions:

  • The D-score offers a robust and cost-effective method for enhancing PTM localization accuracy in mass spectrometry-based proteomics.
  • This approach can be readily applied to various modifications, improving data interpretation without additional experimental or computational burden.
  • The D-score provides a valuable tool for increasing confidence in PTM site assignments.