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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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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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A comprehensive full factorial LC-MS/MS proteomics benchmark data set.

Hans J C T Wessels1, Tom G Bloemberg, Maurice van Dael

  • 1Nijmegen Centre for Mitochondrial Disorders, Department of Laboratory Medicine, Laboratory of Genetic, Endocrine and Metabolic Diseases, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. j.wessels@labgk.umcn.nl

Proteomics
|August 14, 2012
PubMed
Summary

A new liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset was created using Escherichia coli proteins spiked with known proteins. This benchmark dataset aids in developing and validating new proteomics analysis software.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Development of novel analysis methods requires comprehensive LC-MS/MS datasets.
  • Existing datasets may not fully represent real-world challenges in proteomics analysis.

Purpose of the Study:

  • To present a well-annotated, comprehensive LC-MS/MS benchmark dataset.
  • To facilitate the development and evaluation of proteomics data analysis software.
  • To include real-life analytical challenges in the dataset.

Main Methods:

  • Generated 59 LC-MS/MS analyses from 50 protein samples.
  • Used Escherichia coli K12 proteome as a complex background.
  • Spiked samples with known concentrations of bovine carbonic anhydrase II and chicken ovalbumin.
  • Acquired data over two months using multiple columns and calibrations.
  • Included "ground truth" data from pure spike measurements.

Main Results:

  • A comprehensive LC-MS/MS dataset was generated.
  • The dataset incorporates complexity comparable to current proteomic analyses.
  • Real-world analytical challenges were simulated during data acquisition.
  • Included "ground truth" data for accurate method benchmarking.

Conclusions:

  • The presented dataset serves as a valuable resource for benchmarking proteomics analysis methods.
  • This benchmark dataset will aid in the development and validation of new software.
  • Facilitates reproducible and reliable evaluation of quantitative proteomics techniques.