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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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Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries.

Oliver Horlacher1,2, Frederique Lisacek1,2, Markus Müller1,2

  • 1Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.

Journal of Proteome Research
|December 15, 2015
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Summary

New tools, Liberator and MzMod, analyze mass spectrometry data to identify tissue-specific post-translational modifications (PTMs). This approach aids in annotating the function of these crucial protein modifications.

Keywords:
Apache SparkHadoopMS/MSPTMbig datahuman tissuesopen modification searchparallel computingproteomics

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Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Advances in tandem mass spectrometry (MS/MS) have enabled extensive identification of post-translational modifications (PTMs).
  • Open modification searches (OMSs) further expand PTM discovery without prior knowledge of modifications.
  • A significant gap exists in the functional annotation of identified PTMs.

Purpose of the Study:

  • To develop big data mining tools for analyzing large-scale MS/MS datasets.
  • To correlate PTMs with biological metadata from public repositories to bridge the annotation gap.
  • To identify tissue-specific PTMs using newly developed computational tools.

Main Methods:

  • Development of Liberator for building large MS/MS spectrum libraries.
  • Development of MzMod for performing open modification searches (OMS) on these libraries.
  • Application of Liberator and MzMod to a dataset of 25 million MS/MS spectra from 30 human tissues.
  • Comparison of results with existing tools like MODa and X!Tandem.

Main Results:

  • Successful application of Liberator and MzMod to a large-scale human tissue MS/MS dataset.
  • Identification of numerous tissue-specific post-translational modifications.
  • Demonstration of the tools' capability to handle big data challenges in proteomics.

Conclusions:

  • Liberator and MzMod are effective tools for mining large MS/MS datasets to discover PTMs.
  • The approach facilitates the identification of tissue-specific PTMs, aiding functional annotation.
  • Computational analysis of public MS/MS data is a viable strategy to expand PTM functional knowledge.