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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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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Related Experiment Video

Updated: Aug 6, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis.

Witold E Wolski1,2, Paolo Nanni1, Jonas Grossmann1,2

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

Journal of Proteome Research
|March 20, 2023
PubMed
Summary

The prolfqua R package simplifies mass spectrometry-based proteomics analysis, offering quality control, normalization, and statistical modeling for differential expression. It enhances reproducibility and comparison of methods in quantitative proteomics.

Keywords:
differential expression analysisproteomicsstatistical software

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical Computing

Background:

  • Mass spectrometry is crucial for quantitative proteomics and differential protein expression analysis.
  • Existing software tools offer diverse quantification and analysis options.
  • A need exists for a unified, user-friendly R interface for statistical procedures in proteomics.

Purpose of the Study:

  • To develop a modular R package, prolfqua, for mass spectrometry-based differential expression analysis.
  • To integrate quality control, normalization, statistical modeling, and hypothesis testing.
  • To facilitate comparison and understanding of various statistical methods in proteomics.

Main Methods:

  • The prolfqua package integrates a comprehensive workflow for mass spectrometry data analysis.
  • It supports simple and complex experimental designs with multiple factors.
  • Includes functionality for benchmarking different data acquisition, preprocessing, and modeling methods.

Main Results:

  • The prolfqua package provides a clear, consistent, and discoverable API for R.
  • It enables sensitive and specific differential expression analysis.
  • Offers tools for quality control, normalization, protein aggregation, statistical modeling, and sample size estimation.

Conclusions:

  • The prolfqua R package streamlines proteomics data analysis, improving reproducibility and accessibility.
  • It supports diverse experimental designs and facilitates method comparison.
  • The package is available on GitHub under an MIT license for broad use.