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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Dealing with missing values in proteomics data.

Weijia Kong1,2, Harvard Wai Hann Hui1,2, Hui Peng1,2

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

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|November 9, 2022
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Summary
This summary is machine-generated.

Missing values in proteomics data can skew results. This study offers a decision chart to help select the best missing value imputation (MVI) method for accurate analysis, considering data characteristics and potential confounders.

Keywords:
bioinformaticscomputational biologydata analysismissing valuemissing value imputationproteomicsstatistics

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Statistical Analysis

Background:

  • Proteomics datasets frequently contain missing values (MVs), which can compromise statistical power and introduce bias.
  • Existing missing value imputation (MVI) methods vary in assumptions and performance, necessitating careful selection for reliable proteomics analysis.

Purpose of the Study:

  • To address the challenges posed by missing values in proteomics data.
  • To provide guidance for selecting appropriate MVI methods based on dataset characteristics.
  • To highlight the impact of confounders on MVI performance.

Main Methods:

  • Development of a decision chart to aid in the strategic selection of MVI methods.
  • Consideration of various MVI categories and their underlying assumptions.
  • Identification of potential confounders, such as batch effects, that influence MVI.

Main Results:

  • The proposed decision chart facilitates informed choices for MVI method selection.
  • Different MVI methods exhibit variable performance depending on data properties and assumptions.
  • Confounding factors like batch effects can significantly impact the accuracy of imputation.

Conclusions:

  • Strategic selection of MVI methods is crucial for robust proteomics data analysis.
  • Dataset characteristics and potential confounders must be evaluated for optimal MVI.
  • The decision chart serves as a valuable tool for researchers navigating MVI in proteomics.