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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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CoreFlow: a computational platform for integration, analysis and modeling of complex biological data.

Adrian Pasculescu1, Erwin M Schoof2, Pau Creixell2

  • 1Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.

Journal of Proteomics
|February 8, 2014
PubMed
Summary

CoreFlow is an open-source computational platform that streamlines mass spectrometry data analysis for large-scale biology projects. It accelerates data processing, modeling, and reporting, bridging the gap between experimental and computational work.

Keywords:
Computational pipelineData analysisMass spectrometryStatistical analysisWorkflow

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Mass Spectrometry Data Analysis

Background:

  • Managing and integrating large-scale data from mass spectrometry and other biological experiments presents a significant challenge.
  • Rapid advancements in technology necessitate efficient computational tools to keep pace with experimental throughput.

Purpose of the Study:

  • To design and introduce CoreFlow, a computational platform enabling real-time data management and analysis for large-scale biology.
  • To provide programmers with a flexible framework for processing, correcting, and modeling complex biological data.

Main Methods:

  • Developed CoreFlow as a framework for real-time data management, utilizing a MySQL relational database.
  • Enabled custom script creation in R, Python, or Perl for data processing and modeling.
  • Organized scripts into project-specific pipelines with tracked interdependencies and automated report/image generation.

Main Results:

  • CoreFlow reduces the gap between experimental and computational components in biology projects.
  • Accelerated the timeline from data generation to analysis and manuscript preparation.
  • Provided proteomics-specific examples, including corrections for SILAC labeling and MRM/SRM data modeling.

Conclusions:

  • CoreFlow facilitates rapid data analysis through project-specific workflows, enhancing collaboration between programmers and biologists.
  • Its design allows for simultaneous script testing and development, shortening code life cycles.
  • Promotes experimental reproducibility and error reduction in proteomics by enabling full scrutiny of analysis steps.