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Updated: Jan 30, 2026

3D Printing of Preclinical X-ray Computed Tomographic Data Sets
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A Case Study and Methodology for OpenSWATH Parameter Optimization Using the ProCan90 Data Set and 45 810

Sean Peters1, Peter G Hains1,2, Natasha Lucas1

  • 1ProCan , Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney , Westmead , NSW 2145 , Australia.

Journal of Proteome Research
|January 18, 2019
PubMed
Summary
This summary is machine-generated.

Optimizing OpenSWATH pipeline parameters using the ProCan90 dataset improved protein identification by 29.8% in compute-rich settings. A compute-poor approach enhanced run time by 55% with minimal impact on data quality.

Keywords:
Amazon Web ServicesHEK293OpenSWATHProCanbig datamass spectrometryparameter optimizationproteomicsscalabilitysensitivity analysis

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • OpenSWATH is a widely used pipeline for proteomic data analysis.
  • Optimizing algorithm configurations is crucial for maximizing the efficiency and accuracy of proteomic analyses.
  • Large-scale datasets like ProCan90 offer opportunities for systematic parameter optimization.

Purpose of the Study:

  • To demonstrate the optimization of OpenSWATH pipeline parameters using the ProCan90 dataset.
  • To showcase the horizontal scaling of a proteomic analysis pipeline for large-scale computation.
  • To identify optimal parameter sets for both compute-rich and compute-poor environments.

Main Methods:

  • Utilized the ProCan90 dataset, comprising HEK293 technical replicates.
  • Employed Amazon Web Services (AWS) for horizontal scaling, enabling 45,810 OpenSWATH runs.
  • Tested 506 parameter combinations across the dataset, consuming over 340,000 core hours and generating 26 TB of data.

Main Results:

  • A compute-rich parameter set improved protein identifications by 29.8% compared to defaults, with a 103% increase in run time.
  • A compute-poor parameter set achieved a 55% improvement in run time, with a minor 3.4% decrease in protein identifications.
  • Quantification reproducibility and identification confidence remained largely unaffected by the optimized parameters.

Conclusions:

  • The ProCan90 dataset effectively facilitates OpenSWATH parameter optimization.
  • Horizontal scaling on AWS enables efficient, large-scale proteomic data analysis within budget constraints.
  • Tailored parameter sets can balance computational resources, run time, and data analysis outcomes in proteomics.