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Mass Spectrometry: Complex Analysis01:21

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
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Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry.

Stephan Aiche1, Timo Sachsenberg, Erhan Kenar

  • 1Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.

Proteomics
|January 22, 2015
PubMed
Summary

This study integrates OpenMS computational mass spectrometry tools into KNIME workflows for flexible analysis of large proteomics and metabolomics datasets. This enables custom data processing and high-quality visualizations for diverse research tasks.

Keywords:
KNIMEMetabolomicsOpenMSProteomicsWorkflows

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Area of Science:

  • Computational mass spectrometry
  • Proteomics and metabolomics data analysis

Background:

  • Mass spectrometry (MS)-based proteomics and metabolomics are advancing rapidly.
  • Existing monolithic tools lack flexibility for evolving research needs.
  • Workflow systems offer customizable and shareable data processing pipelines.

Purpose of the Study:

  • To integrate OpenMS computational MS tools into the KNIME workflow engine.
  • To enable flexible analysis of large-scale MS datasets.
  • To facilitate the production of high-quality data visualizations.

Main Methods:

  • Integration of OpenMS tools within the KNIME workflow engine.
  • Development of example workflows for specific computational MS tasks.
  • Application to isobaric mass tag quantitation, label-free metabolomics, and proteomics quality control.

Main Results:

  • Demonstrated successful integration of OpenMS and KNIME.
  • Provided functional workflows for diverse MS data analysis tasks.
  • Enabled combined data processing and visualization capabilities.

Conclusions:

  • The integrated OpenMS-KNIME system offers a flexible and powerful platform for computational MS.
  • This approach supports custom-tailored workflows for complex proteomics and metabolomics studies.
  • Facilitates reproducible research through shareable and high-quality data analysis and visualization.