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Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
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Pathomx: an interactive workflow-based tool for the analysis of metabolomic data.

Martin A Fitzpatrick1, Catherine M McGrath2, Stephen P Young3

  • 1Rheumatology Research Group, Centre for Translational Inflammation Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2WD, UK. mxf793@bham.ac.uk.

BMC Bioinformatics
|December 11, 2014
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Summary

Pathomx offers a user-friendly, workflow-based tool for analyzing metabolomic data, integrating various functions for easier processing and visualization. This software enhances accessibility for non-experts and supports complex analyses through scriptable tools.

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Bioinformatics

Background:

  • Metabolomics enables cellular process analysis via small-molecule profiling.
  • Standardized methods have improved reproducibility, but robust data analysis tools remain underdeveloped.
  • Current tools often lack integration, require manual scripting, and demand programming expertise (e.g., MATLAB®, R).

Purpose of the Study:

  • To introduce Pathomx, an intuitive and extensible workflow-based tool for metabolomic data processing, analysis, and visualization.
  • To address the limitations of existing metabolomic analysis tools by providing an integrated and accessible environment.
  • To facilitate complex data analysis for both experts and non-experts in the field.

Main Methods:

  • Pathomx features a core application with a workflow editor, IPython kernel, and a HumanCyc™-derived database.
  • Reusable toolkits can be linked to construct complex analytical workflows.
  • The software includes a base set of plugins for data import, processing, and visualization, with an IPython backend for integration with MATLAB® and R.

Main Results:

  • Pathomx provides an integrated environment for processing, analyzing, and visualizing metabolomic data.
  • Demonstration workflows and datasets are supplied, including an analysis of 1D and 2D (1)H NMR data from mammalian cell growth under hypoxia.
  • The tool's design allows seamless data transfer with existing platforms like MATLAB® and R.

Conclusions:

  • Pathomx serves as a valuable addition to the metabolomic analysis toolkit.
  • Its intuitive interface lowers the barrier to entry for researchers without extensive programming experience.
  • The combination of scriptable tools and integration capabilities supports sophisticated data analysis and encourages community contributions.