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

Donald F. Smith, Carl Schulz, Marco Konijnenburg, Mehmet Kilic, Ron M. A. Heeren*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Web of Science)

Abstract

High-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging enables the spatial mapping and identification of biomolecules from complex surfaces. The need for long time-domain transients, and thus large raw file sizes, results in a large amount of raw data ("big data") that must be processed efficiently and rapidly. This can be compounded by large-area imaging and/or high spatial resolution imaging. For FT-ICR, data processing and data reduction must not compromise the high mass resolution afforded by the mass spectrometer. The continuous mode "Mosaic Datacube" approach allows high mass resolution visualization (0.001?Da) of mass spectrometry imaging data, but requires additional processing as compared to feature-based processing. We describe the use of distributed computing for processing of FT-ICR MS imaging datasets with generation of continuous mode Mosaic Datacubes for high mass resolution visualization. An eight-fold improvement in processing time is demonstrated using a Dutch nationally available cloud service.
Original languageEnglish
Pages (from-to)2321-2327
JournalAnalytical and Bioanalytical Chemistry
Volume407
Issue number8
DOIs
Publication statusPublished - Mar 2015

Keywords

  • Imaging mass spectrometry
  • FTMS
  • Supercomputing
  • Cloud computing
  • Parallel processing
  • MALDI

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