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Related Concept Videos

Proteomics01:33

Proteomics

8.6K
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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Ribosome Profiling02:24

Ribosome Profiling

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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.
Applications of ribosome profiling
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Updated: Oct 25, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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IceR improves proteome coverage and data completeness in global and single-cell proteomics.

Mathias Kalxdorf1,2, Torsten Müller3,4, Oliver Stegle3,5

  • 1German Cancer Research Center, Heidelberg, Germany. mathiaskalxdorf@gmail.com.

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|August 10, 2021
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Summary

Label-free proteomics often has missing values, hindering large-scale studies. Our new IceR (Ion current extraction Re-quantification) workflow significantly reduces missing values, improving protein quantification accuracy and reliability.

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Area of Science:

  • Proteomics
  • Bioinformatics

Background:

  • Label-free proteomics using data-dependent acquisition (DDA) offers unbiased protein quantification but is limited by high missing value rates.
  • This limitation hinders consistent protein quantification in large sample cohorts.

Purpose of the Study:

  • To introduce IceR (Ion current extraction Re-quantification), a novel workflow to address missing values in label-free proteomics.
  • To combine the high identification rates of DDA with the low missing value rates characteristic of data-independent acquisition (DIA).

Main Methods:

  • IceR employs ion current information for a hybrid peptide identification propagation approach.
  • The workflow was evaluated against other quantitative methods using plasma and single-cell proteomics data.

Main Results:

  • IceR demonstrated superior quantification precision, accuracy, reliability, and data completeness compared to existing workflows.
  • The application of IceR enhanced the number of reliably quantified proteins in plasma and single-cell proteomic datasets.
  • IceR improved the discriminability between single-cell populations and enabled the reconstruction of developmental trajectories.

Conclusions:

  • IceR effectively reduces missing values in label-free proteomics, enhancing data completeness and reliability.
  • This workflow is beneficial for large-scale global proteomics and low-input applications.
  • IceR is available as an R-package, promoting its accessibility and user-friendliness for the research community.