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Related Concept Videos

Proteomics01:33

Proteomics

7.6K
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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Related Experiment Video

Updated: Jul 31, 2025

Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis
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Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis

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Statistical approaches applicable in managing OMICS data: Urinary proteomics as exemplary case.

De-Wei An1,2, Yu-Ling Yu1,2, Dries S Martens3

  • 1Non-Profit Research Association Alliance for the Promotion of Preventive Medicine, Mechelen, Belgium.

Mass Spectrometry Reviews
|May 5, 2023
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Summary

This review outlines a workflow for analyzing omics data in large populations using urinary proteomics profiling (UPP). It details steps from study planning to identifying personalized intervention targets for disease prevention and treatment.

Keywords:
multidimensional classifiersproteomicsstatistical methodsurinary proteomics

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Area of Science:

  • Biomarker Discovery
  • Proteomics
  • Population Health

Background:

  • Omics technologies generate vast datasets.
  • Analyzing omics data in large populations presents unique challenges.
  • Standardized workflows are needed for robust biomarker analysis.

Purpose of the Study:

  • To describe a comprehensive workflow for analyzing omics data in large study populations.
  • To illustrate the application of this workflow using urinary proteomics profiling (UPP).
  • To guide the integration of omics biomarkers into clinical practice for disease prevention and treatment.

Main Methods:

  • Detailed workflow for omics data analysis, including study planning, data preparation, preprocessing, statistical curation, and covariable selection.
  • Methods for relating omics markers to health outcomes and assessing their diagnostic/prognostic value.
  • Pathway analysis for identifying personalized intervention targets.

Main Results:

  • The proposed workflow covers all essential steps for analyzing omics data in large cohorts.
  • It demonstrates how to evaluate the added value of omics biomarkers beyond traditional risk factors.
  • The workflow facilitates the identification of potential targets for personalized medicine.

Conclusions:

  • Analysis of omics biomarkers for health outcomes follows established statistical principles for large cohort studies.
  • Careful planning of database structure, curation, and analysis is crucial due to the high dimensionality of omics data.
  • This workflow supports the effective use of omics data for advancing personalized disease prevention and treatment strategies.