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Related Concept Videos

Proteomics01:33

Proteomics

7.2K
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...
7.2K

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Related Experiment Video

Updated: May 31, 2025

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
09:00

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

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prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative

Witold E Wolski1,2, Jonas Grossmann1,2, Leonardo Schwarz1,2

  • 1Functional Genomics Center Zurich (FGCZ) - University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.

Journal of Proteome Research
|January 24, 2025
PubMed
Summary
This summary is machine-generated.

Mass spectrometry is key for quantitative proteomics. The prolfquapp tool simplifies differential expression analysis (DEA) for complex experiments, offering accessible, integrated data processing and visualization for researchers.

Keywords:
differential expression analysisproteomicsstatistical softwareworkflows

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry is fundamental for quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA).
  • Interactive DEA tools become impractical for complex experiments with numerous samples, groups, and identified proteins.

Purpose of the Study:

  • To develop a command-line interface tool, prolfquapp, that simplifies DEA for large-scale quantitative proteomics.
  • To enable nonprogrammers to perform DEA and integrate it into workflow management systems.
  • To streamline data processing and result visualization for complex proteomics experiments.

Main Methods:

  • Prolfquapp provides a command-line interface for DEA.
  • It generates dynamic HTML reports for exploring differential expression results.
  • It leverages advanced statistical models from the prolfqua R package.

Main Results:

  • Prolfquapp simplifies DEA, making it accessible to nonprogrammers.
  • Dynamic HTML reports facilitate the exploration of complex experimental results, including repeated measurements and multiple explanatory variables.
  • Supports multiple output formats (XLSX, SummarizedExperiment, rank files) for further analysis.

Conclusions:

  • Prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics.
  • It combines efficient data processing with insightful, publication-ready outputs.
  • Facilitates further interactive analysis using spreadsheet software, Shiny applications, or gene set enrichment analysis tools.