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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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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Updated: Feb 19, 2026

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

Tobias Schmidt1, Patroklos Samaras1, Martin Frejno1

  • 1Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, 85354 Bavaria, Germany.

Nucleic Acids Research
|November 7, 2017
PubMed
Summary
This summary is machine-generated.

ProteomicsDB is an in-memory database for exploring quantitative proteomics data. It now integrates transcriptomics and other omics data, offering a multi-purpose resource for researchers.

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Area of Science:

  • Bioinformatics
  • Proteomics
  • Genomics

Background:

  • ProteomicsDB has been a key resource for exploring quantitative mass spectrometry-based proteomics data since 2014.
  • It initially focused on the human proteome and has grown to include data from over 19,000 LC-MS/MS experiments across 78 projects.

Purpose of the Study:

  • To enhance ProteomicsDB by integrating diverse quantitative omics data beyond proteomics.
  • To transform ProteomicsDB into a multi-purpose resource connecting protein quantification with various metadata.
  • To improve researchers' ability to explore protein expression across different biological contexts.

Main Methods:

  • Extended the ProteomicsDB data model to accommodate additional quantitative omics data types.
  • Integrated transcriptomics data (e.g., from NCBI GEO), protein-protein interactions (STRING), functional annotations (KEGG), and drug-related data.
  • Developed a standardized analysis pipeline for comparing multiple datasets and facilitating protein expression exploration.
  • Implemented a user-friendly interface for both protein-centric and multi-protein-centric data navigation.

Main Results:

  • ProteomicsDB now houses quantitative data from 78 projects, totaling over 19,000 LC-MS/MS experiments.
  • The database integrates transcriptomics, protein-protein interactions, functional annotations, and drug sensitivity data.
  • The platform facilitates the comparison of protein expression across hundreds of tissues, cell lines, and body fluids.
  • A comprehensive API allows systematic access to quantitative data.

Conclusions:

  • ProteomicsDB has evolved into a versatile, multi-omics data resource.
  • The integration of diverse datasets enhances its utility for researchers studying protein expression and function.
  • The platform provides powerful tools for navigating and analyzing complex biological data.