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Related Concept Videos

Protein Networks02:26

Protein Networks

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: Jun 12, 2026

Identification of protein complexes with quantitative proteomics in S. cerevisiae
11:12

Identification of protein complexes with quantitative proteomics in S. cerevisiae

Published on: March 4, 2009

Meta-analysis for protein identification: a case study on yeast data.

Roger Higdon1, Winston Haynes, Eugene Kolker

  • 1Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington 98101, USA.

Omics : a Journal of Integrative Biology
|June 24, 2010
PubMed
Summary
This summary is machine-generated.

Combining mass spectrometry (MS) proteomics data requires careful error rate assessment. Failure to reevaluate the false discovery rate (FDR) can lead to underestimation, impacting protein identification accuracy.

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Last Updated: Jun 12, 2026

Identification of protein complexes with quantitative proteomics in S. cerevisiae
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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Publicly available mass spectrometry (MS) proteomics datasets are abundant.
  • Methods for combining these datasets and assessing protein identification error rates are underdeveloped.

Purpose of the Study:

  • To investigate the impact of data variation on yeast proteome coverage.
  • To evaluate estimation of the false discovery rate (FDR) when combining proteomics data.

Main Methods:

  • Analysis of a subset of publicly available yeast MS proteomics data.
  • Comparison of FDR estimation methods, including a weighted model and worst-case approximation.

Main Results:

  • Combining protein IDs without reevaluating FDR underestimated it threefold.
  • A weighted model improved protein ID counts at a 1% FDR compared to other meta-analysis methods.
  • FDRs above 1% led to a high rate of false discoveries.

Conclusions:

  • Reevaluating FDR is crucial when merging proteomics datasets.
  • Worst-case FDR estimation offers a reasonable approximation when raw data is unavailable.
  • Prioritizing high-quality experiments with unique identifications enhances meta-analysis efficiency.