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Related Concept Videos

Proteomics01:33

Proteomics

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 proteomics...

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Analysis of high accuracy, quantitative proteomics data in the MaxQB database.

Christoph Schaab1, Tamar Geiger, Gabriele Stoehr

  • 1Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, D-82152 Martinsried, Germany.

Molecular & Cellular Proteomics : MCP
|February 4, 2012
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Summary

MaxQB is a novel database for storing and analyzing large proteomics projects, enabling cross-experiment comparisons and quality control. It helps manage proteomic data challenges like false positives and integrates quantitative information effectively.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry (MS)-based proteomics yields vast quantitative data, but challenges remain in comparing and storing information across experiments.
  • Existing methods struggle with database contamination from low-quality peptide identifications, inflated false positives in combined datasets, and integrating quantitative data.
  • There is a need for a dedicated database solution for high-resolution, quantitative proteomic data.

Purpose of the Study:

  • To introduce MaxQB, a novel database designed for storing, displaying, and jointly analyzing large-scale proteomics projects.
  • To demonstrate MaxQB's utility in comparing proteome measurements across different experiments and controlling data quality.
  • To provide a user-friendly platform for accessing high-resolution proteomic data and analysis tools.

Main Methods:

  • Development of the MaxQB database, specifically designed for high-resolution and quantitative proteomic data.
  • Demonstration using proteomic data from 11 human cell lines and 28 mouse tissues.
  • Implementation of database-wide false discovery rate control by adjusting project-specific cutoff scores for combined datasets.
  • Utilizing label-free quantification for visualizing protein expression levels across cell lines and tissues.
  • Calculation of signal reproducibility of detected peptides across different proteomes using Spearman rank correlation.

Main Results:

  • MaxQB successfully stores and displays large collections of proteomic projects, enabling joint analysis and comparison.
  • The 11 cell line proteomes analyzed in MaxQB identified proteins expressed from over half of all human genes.
  • Protein expression levels and rank order were visualized across cell lines, and signal reproducibility analysis helped pinpoint false protein identifications.
  • Spearman rank correlation between peptide intensity and detection probability exceeded 0.8 for 64% of the proteome, indicating high reproducibility.

Conclusions:

  • MaxQB addresses key challenges in managing and analyzing large-scale proteomic datasets, including data quality and quantitative integration.
  • The database facilitates cross-experiment comparisons, enabling robust identification and quantification of proteins.
  • MaxQB provides a valuable resource for the scientific community, offering access to high-resolution data and analysis tools via a web interface.