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

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Pathomx : an interactive workflow-based tool for the analysis of metabolomic data. / Fitzpatrick, Martin A; McGrath, Catherine M; Young, Stephen P.

In: BMC Bioinformatics, Vol. 15, No. 1, 396, 10.12.2014.

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@article{83503592e2304cf5966ba425c26db97f,
title = "Pathomx: an interactive workflow-based tool for the analysis of metabolomic data",
abstract = "BackgroundMetabolomics 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{\circledR} 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.ResultsThe 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{\circledR} 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 1H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions.ConclusionsPathomx 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.",
keywords = "Metabolomics, Omics, nmr, Analysis, Visualisation, Workflow, Automation, Python",
author = "Fitzpatrick, {Martin A} and McGrath, {Catherine M} and Young, {Stephen P}",
year = "2014",
month = "12",
day = "10",
doi = "10.1186/s12859-014-0396-9",
language = "English",
volume = "15",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - Pathomx

T2 - an interactive workflow-based tool for the analysis of metabolomic data

AU - Fitzpatrick, Martin A

AU - McGrath, Catherine M

AU - Young, Stephen P

PY - 2014/12/10

Y1 - 2014/12/10

N2 - BackgroundMetabolomics 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.ResultsThe 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 1H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions.ConclusionsPathomx 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.

AB - BackgroundMetabolomics 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.ResultsThe 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 1H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions.ConclusionsPathomx 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.

KW - Metabolomics

KW - Omics

KW - nmr

KW - Analysis

KW - Visualisation

KW - Workflow

KW - Automation

KW - Python

U2 - 10.1186/s12859-014-0396-9

DO - 10.1186/s12859-014-0396-9

M3 - Article

C2 - 25490956

VL - 15

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 1

M1 - 396

ER -