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BMC Bioinformatics. 2014 Dec 10;15:396. doi: 10.1186/s12859-014-0396-9.

Pathomx: an interactive workflow-based tool for the analysis of metabolomic data.

Author information

1
Rheumatology Research Group, Centre for Translational Inflammation Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2WD, UK. mxf793@bham.ac.uk.
2
Rheumatology Research Group, Centre for Translational Inflammation Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2WD, UK. c.m.mcgrath@bham.ac.uk.
3
Rheumatology Research Group, Centre for Translational Inflammation Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2WD, UK. s.p.young@bham.ac.uk.

Abstract

BACKGROUND:

Metabolomics is a systems approach to the analysis of cellular processes through small-molecule metabolite profiling. Standardisation of sample handling and acquisition approaches has contributed to reproducibility. However, the development of robust methods for the analysis of metabolomic data is a work-in-progress. The tools that do exist are often not well integrated, requiring manual data handling and custom scripting on a case-by-case basis. Furthermore, existing tools often require experience with programming environments such as MATLAB® or R to use, limiting accessibility. Here we present Pathomx, a workflow-based tool for the processing, analysis and visualisation of metabolomic and associated data in an intuitive and extensible environment.

RESULTS:

The core application provides a workflow editor, IPython kernel and a HumanCyc™-derived database of metabolites, proteins and genes. Toolkits provide reusable tools that may be linked together to create complex workflows. Pathomx is released with a base set of plugins for the import, processing and visualisation of data. The IPython backend provides integration with existing platforms including MATLAB® and R, allowing data to be seamlessly transferred. Pathomx is supplied with a series of demonstration workflows and datasets. To demonstrate the use of the software we here present an analysis of 1D and 2D (1)H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions.

CONCLUSIONS:

Pathomx is a useful addition to the analysis toolbox. The intuitive interface lowers the barrier to entry for non-experts, while scriptable tools and integration with existing tools supports complex analysis. We welcome contributions from the community.

PMID:
25490956
PMCID:
PMC4271363
DOI:
10.1186/s12859-014-0396-9
[Indexed for MEDLINE]
Free PMC Article

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