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Related Concept Videos

Proteomics01:33

Proteomics

10.0K
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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

Updated: Mar 12, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Tag-Count Analysis of Large-Scale Proteomic Data.

Owen E Branson1,2,3, Michael A Freitas1,2,3

  • 1The Ohio State Biochemistry Graduate Program, The Ohio State University , Columbus, Ohio 43210, United States.

Journal of Proteome Research
|November 1, 2016
PubMed
Summary
This summary is machine-generated.

Spectral counting (SpC) offers a robust, cost-effective method for label-free proteomics. This study demonstrates SpC

Keywords:
RbioconductoredgeRlabel-freemass spectrometryproteomicsspectral countingtag-count

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Area of Science:

  • Proteomics
  • Quantitative Biology
  • Bioinformatics

Background:

  • Label-free quantitative methods are essential in bottom-up proteomics for their robustness and cost-effectiveness.
  • Existing methods like peak-abundance-based quantitation (e.g., MaxLFQ) require computationally intensive precursor peak alignment, limiting their application to low-resolution data.
  • Spectral counting (SpC) presents an alternative label-free approach, using peptide identification counts to infer protein abundance.

Purpose of the Study:

  • To evaluate the suitability of spectral counting for differential protein expression analysis in large-scale discovery proteomics.
  • To demonstrate a statistical modeling approach for spectral count data.

Main Methods:

  • Utilized spectral counts from multidimensional proteomic datasets.
  • Modeled the mean-dispersion relationship of spectral counts using the edgeR statistical package.
  • Simulated spectral count data to validate the approach for large-scale datasets.

Main Results:

  • Spectral counts exhibit a mean-dispersion relationship amenable to statistical modeling with edgeR.
  • The proposed spectral counting approach is suitable for routine application in large-scale discovery proteomics.
  • Differential protein expression can be reliably determined using this method.

Conclusions:

  • Spectral counting provides a computationally efficient and robust alternative for quantitative proteomics.
  • Statistical modeling of spectral counts enables accurate differential protein expression analysis.
  • This approach facilitates cost-effective, large-scale proteomic studies.