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Distributed computing strategies for processing of FT-ICR MS imaging datasets for continuous mode data visualization.

Donald F Smith1, Carl Schulz, Marco Konijnenburg

  • 1FOM Institute AMOLF, Science Park 104, 1098 XG, Amsterdam, The Netherlands, donsmith@magnet.fsu.edu.

Analytical and Bioanalytical Chemistry
|October 3, 2014
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Summary

High-resolution mass spectrometry imaging generates big data. This study uses distributed computing to efficiently process Fourier transform ion cyclotron resonance (FT-ICR) MS imaging data, significantly reducing processing time for high-resolution visualization.

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Area of Science:

  • Analytical Chemistry
  • Biophysics
  • Computational Biology

Background:

  • High-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging is crucial for spatial mapping of biomolecules.
  • Large datasets generated by FT-ICR MS imaging require efficient processing to maintain high mass resolution.
  • Current processing methods can be time-consuming, especially for large-area or high-spatial-resolution imaging.

Purpose of the Study:

  • To describe the application of distributed computing for processing FT-ICR MS imaging datasets.
  • To enable the generation of continuous mode Mosaic Datacubes for high mass resolution visualization.
  • To demonstrate an improvement in data processing efficiency for FT-ICR MS imaging.

Main Methods:

  • Utilized distributed computing infrastructure for parallel processing of large FT-ICR MS imaging datasets.
  • Implemented data processing workflows to generate continuous mode Mosaic Datacubes.
  • Leveraged a Dutch nationally available cloud service for computational resources.

Main Results:

  • Achieved an eight-fold improvement in data processing time for FT-ICR MS imaging datasets.
  • Successfully generated continuous mode Mosaic Datacubes, facilitating high mass resolution visualization (0.001 Da).
  • Demonstrated the feasibility and efficiency of distributed computing for handling big data in FT-ICR MS imaging.

Conclusions:

  • Distributed computing offers a viable solution for accelerating the processing of large FT-ICR MS imaging datasets.
  • The Mosaic Datacube approach, combined with distributed computing, enhances high-resolution visualization capabilities.
  • This approach is critical for advancing the application of FT-ICR MS imaging in complex biological and chemical analyses.