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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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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Adaption of the global test idea to proteomics data with missing values.

Klaus Jung1, Hassan Dihazi, Asima Bibi

  • 1Department of Medical Statistics, and Department of Nephrology and Rheumatology, University Medical Center Göttingen, Göttingen 37099, Germany.

Bioinformatics (Oxford, England)
|February 4, 2014
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Summary

This study introduces a novel mixed linear model for global proteomics analysis, addressing missing data challenges. The method successfully ranks Gene Ontology terms and identifies correlations between protein deficiencies and biological processes.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Global test procedures are standard in gene expression analysis but underutilized in proteomics.
  • Challenges in proteomics include obtaining functional annotations and handling missing values.
  • High-throughput proteome expression data requires adapted analytical methods.

Purpose of the Study:

  • To adapt global test procedures for high-throughput proteomics data analysis.
  • To develop a method robust to missing values in proteomic datasets.
  • To enable functional annotation enrichment analysis for protein sets.

Main Methods:

  • A mixed linear model combined with permutation testing and missing value imputation.
  • Utilizes the R-package 'RepeatedHighDim' for implementation.
  • Handles proteins represented by multiple spots on 2-D gels as protein sets.

Main Results:

  • Simulation studies demonstrate the effectiveness of imputation in correcting bias caused by missing values.
  • The approach allows for ranking of Gene Ontology (GO) terms associated with protein sets.
  • Analysis revealed correlations between 'calreticulin' deficiency and heart muscle biological processes.

Conclusions:

  • The proposed mixed linear model with imputation is a viable approach for global tests in proteomics.
  • This method enhances the analysis of functional relationships in proteomic data.
  • The findings suggest potential links between specific protein deficiencies and cellular functions.