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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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MS1-based label-free proteomics using a quadrupole orbitrap mass spectrometer.

Tali Shalit1, Dalia Elinger1, Alon Savidor1

  • 1†de Botton Institute for Protein Profiling and ‡Ilana and Pascal Mantoux Institute for Bioinformatics, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot 76100, Israel.

Journal of Proteome Research
|March 18, 2015
PubMed
Summary

This study presents a benchmark dataset for label-free quantitative proteomics using a quadrupole orbitrap mass spectrometer. The data demonstrates high precision and accuracy for MS1-intensity-based quantification and aids in developing proteomics informatics tools.

Keywords:
informatics toolslabel-free proteomicsquantification

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Label-free quantitative proteomics is crucial for analyzing complex biological samples.
  • Benchmarking analytical platforms and software is essential for reliable proteomic data.

Purpose of the Study:

  • To present a dataset for benchmarking MS1-intensity-based label-free quantitative proteomics.
  • To evaluate the performance of a quadrupole orbitrap mass spectrometer and associated informatics tools.
  • To compare different protein inference methods.

Main Methods:

  • A data set was generated using a quadrupole orbitrap mass spectrometer.
  • Escherichia coli digest was spiked into HeLa digest at varying concentrations.
  • MS1 intensity-based label-free quantification was performed.
  • Quantitative protein inference methods (iBAQ, Hi-N) were compared.

Main Results:

  • The presented instrumentation and informatics combination achieved high precision and quantification accuracy.
  • The dataset allows for the evaluation of MS1-intensity-based label-free quantification.
  • Comparisons of iBAQ and Hi-N protein inference methods were conducted.

Conclusions:

  • The developed proteomic platform demonstrates high performance for label-free quantification.
  • The dataset serves as a valuable resource for optimizing proteomics informatics tools.
  • Raw data are publicly available for further research and development.