Pathomx
Pathomx is workflow-based scientific data processing, analysis and visualisation too. Built on IPython notebooks it allows rapid prototyping of analysis approaches, sharing of workflows and export of generated IPython notebooks that capture the approach. The included notebook tools are aimed towards the analysis of metabolomic data inlucindg: NMR data processing, integration with the BioCyc database, dynamic metabolic pathway drawing and support for GPML/KEGG.
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 1H 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.
