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SECIMTools: a suite of metabolomics data analysis tools.

Alexander S Kirpich1,2,3,4, Miguel Ibarra1,2, Oleksandr Moskalenko5

  • 1Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.

BMC Bioinformatics
|April 22, 2018
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Summary

SECIMTools provides an accessible, integrated workflow for metabolomics data analysis on the Galaxy platform. This enhances reproducibility and facilitates personalized medicine research through advanced bioinformatics tools.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Metabolomics

Background:

  • Metabolomics research holds significant promise for advancing personalized medicine.
  • High-throughput untargeted metabolomics analysis requires accessible, user-friendly analytical tools.
  • The Galaxy platform offers an open-access environment for big data interaction and workflow reproducibility.

Purpose of the Study:

  • To develop and present SECIMTools, a suite of bioinformatics applications for metabolomics data analysis.
  • To integrate these tools within the Galaxy platform for enhanced accessibility and workflow creation.
  • To provide a comprehensive set of analytical methods for metabolomics studies.

Main Methods:

  • SECIMTools comprises Python applications adaptable for standalone use or Galaxy integration.
  • The suite offers quality control, visualization, statistical analysis, and machine learning methods.
  • Tools include hierarchical clustering, PCA, PLS-DA, random forest, and LASSO variable selection.

Main Results:

  • SECIMTools provides a comprehensive toolkit for various stages of metabolomics data analysis.
  • Integration with Galaxy allows for the creation of customizable and reproducible analytical workflows.
  • The platform supports standard data formats for flexible tool integration.

Conclusions:

  • SECIMTools, via the Galaxy platform, enables integrated and interpretable metabolomics data analysis workflows.
  • The modular design encourages novel workflow development and facilitates collaboration.
  • The framework supports future integration with other omics data for multi-omics studies.