Pathomx: an interactive workflow-based tool for the analysis of metabolomic data
© Fitzpatrick et al.; licensee BioMed Central Ltd. 2014
Received: 21 March 2014
Accepted: 24 November 2014
Published: 10 December 2014
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.
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.
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.
KeywordsMetabolomics Omics nmr Analysis Visualisation Workflow Automation Python
Metabolomics is a systems approach to the analysis of cellular processes through small-molecule metabolite profiles of a cell, tissue, organ or organism that results from the combined action of proteome, transcriptome and genome . Metabolomics can be split broadly into targeted and untargeted approaches. Targeted metabolomics uses focused study of known pathways, reactions or metabolites in in vitro cell models and has been used to gain insight into metabolic requirements and vulnerabilities of cancer cells . Untargeted metabolomics is a hypothesis-forming approach in which datasets derived from biological fluids are queried using multivariate analysis techniques, with the goal of identifying biomarkers or metabolic changes that can inform future study. This approach has been successfully employed for the identification of novel disease markers .
The standardisation of sample handling and data acquisition has contributed to improved reproducibility in metabolomics . Data analysis methods in contrast are less well defined. Existing tools commonly build on mathematical environments, such as MATLAB® or R and require a level of familiarity not usually available in those from non-mathematical backgrounds. The difficulties moving data between these environments and associated packages is a hindrance to an integrated workflow. In our own group we have used this type of hybrid platform, combining MATLAB®-based NMRLab and MetaboLab  for processing and PLS Toolbox (Eigenvector Research, Wenatchee WA USA) for multivariate analysis, with Chenomx (Edmonton, Alberta, Canada) and the Human Metabolome Database  for metabolite identification. It is our experience that the complexity of the analysis workflow acts as a significant barrier to the use of metabolomics by non-experts, hinders discovery and slows throughput.
These issues are not unique to metabolomic analysis and the preceding decade has seen work to address them within the bioinformatics field. Scientific workflow tools have emerged in recent years as a powerful and flexible approach to the analysis of large datasets . Automation of workflows can contribute to the reproducibility of analysis and reduction in error, while simultaneously increasing throughput. The major workflow analysis platforms in current use are Taverna  and Galaxy , which have established themselves as key tools in the bioinformaticians' toolkit. Both share a common approach of stepwise workflow-construction paired with server-based batch processing, yet differ on the level of abstraction of their components. Taverna is a low-level workflow creator, offering construction of complex functions from discrete algorithmic steps and with a particular focus on remote service integration. Galaxy in contrast offers high-level components that perform common bioinformatics tasks wholesale, with a focus on local-service integration and the need for no programming experience. Both platforms have been developed with a focus on genomic and transcriptomics analysis and lack support for the analysis of metabolomic data. The batch-based processing paradigm also limits application to the steps of analysis that can be fully automated while the latter stages of metabolomic data analysis are typically more exploratory, with iterative application of multivariate techniques, interrogation of biological databases, and pathway visualisation for interpretation of the data. Tools are already available to aid in the various stages of metabolomic data analysis, with MetaboAnalyst , a web-based metabolomic analysis pipeline, being of particular note. It includes modules for enrichment, pathway and time-series analysis, and has a particular focus on usability with the complete pipeline configurable through a simple web-based interface. However, this simplicity does come at the cost of the adaptability and automation that workflow analysis can offer. Further, the inability to adapt or extend analysis modules means that complete analysis of a dataset will often require other tools.
Recognising the benefits that workflow-based analysis could offer to metabolomics analysis while hoping to overcome the limitations of batch-based processing, we developed Pathomx: a workflow-based tool for data analysis. The software is designed to be adaptable, intuitive and to integrate well with existing tools and pipelines, acting as the essential glue in the metabolomics toolbox.
Pathomx is an open source and cross-platform analysis tool. It is developed in Python (v2.7; Python Software Foundation) with a graphical user interface (GUI) based on Qt (v5.1; Digia) and graphing powered by Matplotlib (v1.1.1) . The processing kernel is based on IPython (v3.0.0).
In Pathomx nomenclature plugins provide tools that are then used for construction of workflows. The software ships with a base set of tools for data import, processing, analysis, visualisation and export based on the NumPy (v1.7.1), SciPy (v0.12.0) , Pandas (v0.14.1), SkiKit-Learn (v0.15.1)  and NMRGlue (v.0.4)  Python packages. Many of the algorithms in the default toolkit have subsequently been released as standalone Python packages to allow use outside Pathomx. These include biocyc (v0.1.0) a Python BioCyc API, gpml2svg (v0.3.0) a GPML renderer, icoshift (v0.6.0) a Python implementation of the Icoshift algorithm , metaboviz (v0.0.3) a metabolic pathway drawing package utilising the pydot (v1.0.28) interface to Graphviz (v2.12)  and pathminer (v0.0.2) a metabolic pathway mining algorithm. The functionality described in this paper relates to the base plugins provided with Pathomx 3.0.
Data analysis workflows are constructed using a drag-and-drop interface. Dragging a tool from the toolkit creates a new tool in the workflow. Selecting the tool allows configuration options to be changed, data sources to be configured and the tool code to be run. Inputs can also be managed directly from the workflow editor by dragging the output of one tool into the appropriate input of another. Recalculation and regeneration of figures is dynamic and the current run-state is visualised within the editor (blue = complete, red = error, green = active). The default toolkit makes extensive use of Pandas DataFrames and standard structures to allow tools to communicate easily. Tools can make use of parallel processing to allow efficient execution of complex workflows on large datasets on a standard modern desktop machine. Errors are flagged with both descriptive text and kernel backtraces for debugging purposes. Source code is available for all tools and can be modified using the inline editor to tweak behaviour. Resulting workflows can be exported as standalone scripts to run independently of the Pathomx environment. Figures can be exported as high-resolution TIF files for publication. Interfaces to both MATLAB® and R are available through the IPython backend allowing data to be passed between environments as required. Data may be imported from a number of other tools and public databases, including Metabolights  and Gene Expression Omnibus (GEO) .
Pathomx includes a subset of the HumanCyc™ Homo sapiens pathway data available under license from SRI International . Database cross-referencing is supported for KEGG , HMDB  and other databases, generated from BioCyc annotations and the MNXref database .
Results and discussion
Prerequisite to the analysis of 1D NMR data are a number of spectral processing steps that ensure that any observed variation in the data is reflective of biology. An example 1D NMR analysis workflow is included that performs these steps with the included Bruker-format NMR output. 1D NOESY 1H NMR spectra are first peak-aligned using a TMSP reference peak and then spectra were further aligned using the Icoshift correlation-shifting segmental alignment algorithm. Spectra were then binned at 0.015 ppm and assigned to experimental groups (Figure 1).
Identification and quantification of the 2D JRES data was performed using the BML-NMR service and the resulting data can be loaded automatically into Pathomx. Identification of metabolites in 1D data is typically more involved and Pathomx includes support for both manual peak assignment and automated peak-metabolite quantification with Chenomx. However, in the provided workflow we have used MetaboHunter  a free remote web service which identifies metabolites using peak-matching to the HMDB (Figure 4A).
Metabolic pathway analysis
Pathomx is a workflow-based tool for the exploration of metabolomic data. It supports a complete processing workflow through data import, processing, analysis and visualisation. It is open source and features a plugin system that can be readily extended with new features and integrates readily with existing tools. Workflow construction requires no prior programming knowledge but can utilise it where available. The resulting workflows can be shared and re-used or exported as standalone Python scripts. Plugin development is supported through a simple, well-documented Python-based API. We welcome contributions of plugins and workflows from the community.
Availability and requirements
Project name: Pathomx
Project home page: http://pathomx.org
Platform: Binaries are available for download on Windows and MacOS X. Installation on Linux (Ubuntu) is supported via PyPi. Source code is available.
Programming language: Python 2.7, Qt 5.2
Other requirements: Package binary contains all requirements
License: GNU GPL v3.0
Any restrictions to use by non-academics: N/A
MA Fitzpatrick, PhD student, University of Birmingham.
SP Young, Senior Lecturer, University of Birmingham.
C McGrath, Academic Clinical Fellow, University of Birmingham.
This work was supported by a Wellcome Trust studentship to MAF (grant 14844) and an Academic Clinical Fellowship from the National Institute for Health Research to CMMcG. Dr Dan Tulpan of National Research Council Canada, Institute for Information Technology provided invaluable help during the development of the Metabohunter plugin, used here in the processing of the 1D data. Development of Pathomx would not have been possible without the availability of the open-source tools on which it is built. We extend our thanks to the developers of the freely available libraries we have used.
- Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB: Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 2004, 22: 245-252. 10.1016/j.tibtech.2004.03.007.View ArticlePubMedGoogle Scholar
- Armitage EG, Barbas C: Metabolomics in cancer biomarker discovery: Current trends and future perspectives. J Pharm. Biomed Anal 2013.,
- Fitzpatrick M, Young SP: Metabolomics - a novel window into inflammatory disease. Swiss Med Wkly. 2013, 143: 1-12.Google Scholar
- Salek RM, Haug K, Conesa P, Hastings J, Williams M, Mahendraker T, Maguire E, González-Beltrán AN, Rocca-Serra P, Sansone S-A, Steinbeck C: The MetaboLights repository: curation challenges in metabolomics. Database (Oxford). 2013, 2013: bat029-10.1093/database/bat029.View ArticleGoogle Scholar
- Ludwig C, Günther UL: MetaboLab–advanced NMR data processing and analysis for metabolomics. BMC Bioinformatics. 2011, 12: 366-10.1186/1471-2105-12-366.View ArticlePubMed CentralPubMedGoogle Scholar
- Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A: HMDB 3.0–The Human Metabolome Database in 2013. Nucleic Acids Res. 2013, 41: D801-D807. 10.1093/nar/gks1065.View ArticlePubMed CentralPubMedGoogle Scholar
- Curcin V, Ghanem M: Scientific workflow systems - can one size fit all?. Cairo Int Biomed Eng Conf. 2008, 2008: 1-9. 18-20 DecemberGoogle Scholar
- Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock MR, Wipat A, Li P: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics. 2004, 20: 3045-3054. 10.1093/bioinformatics/bth361.View ArticlePubMedGoogle Scholar
- Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ, Nekrutenko A: Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005, 15: 1451-1455. 10.1101/gr.4086505.View ArticlePubMed CentralPubMedGoogle Scholar
- Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS: MetaboAnalyst 2.0-a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012, 40: W127-W133. 10.1093/nar/gks374.View ArticlePubMed CentralPubMedGoogle Scholar
- Hunter JD: Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007, 9: 90-95. 10.1109/MCSE.2007.55.View ArticleGoogle Scholar
- Oliphant TE: Python for scientific computing. Comput Sci Eng. 2007, 9: 10-20. 10.1109/MCSE.2007.58.View ArticleGoogle Scholar
- Pedregosa F, Weiss R, Brucher M: Scikit-learn: machine learning in python. J Mach Learn Res. 2011, 12: 2825-2830.Google Scholar
- Helmus JJ, Jaroniec CP: Nmrglue: an open source Python package for the analysis of multidimensional NMR data. J Biomol NMR. 2013, 55: 355-367. 10.1007/s10858-013-9718-x.View ArticlePubMed CentralPubMedGoogle Scholar
- Savorani F, Tomasi G, Engelsen SB: Icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson. 2010, 202: 190-202. 10.1016/j.jmr.2009.11.012.View ArticlePubMedGoogle Scholar
- Gansner E, North SC: An open graph visualization system and its applications to software engineering. Softw - Pract Exp. 1999, 00: 1-5.Google Scholar
- Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, Mahendraker T, Williams M, Neumann S, Rocca-Serra P, Maguire E, González-Beltrán A, Sansone S-A, Griffin JL, Steinbeck C: MetaboLights–an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 2013, 41: D781-D786. 10.1093/nar/gks1004.View ArticlePubMed CentralPubMedGoogle Scholar
- Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30: 207-210. 10.1093/nar/30.1.207.View ArticlePubMed CentralPubMedGoogle Scholar
- Trupp M, Altman T, Fulcher CA, Caspi R, Krummenacker M, Paley S, Karp PD: Beyond the genome ( BTG ) is a ( PGDB ) pathway genome databasets. HumanCyc. 2010, 11: 2010-Google Scholar
- Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40: D109-D114. 10.1093/nar/gkr988.View ArticlePubMed CentralPubMedGoogle Scholar
- Bernard T, Bridge A, Morgat A, Moretti S, Xenarios I, Pagni M: Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform. 2014, 15: 123-135. 10.1093/bib/bbs058.View ArticlePubMed CentralPubMedGoogle Scholar
- Wu H, Southam AD, Hines A, Viant MR: High-throughput tissue extraction protocol for NMR- and MS-based metabolomics. Anal Biochem. 2008, 372: 204-212. 10.1016/j.ab.2007.10.002.View ArticlePubMedGoogle Scholar
- Ludwig C, Easton J, Lodi A, Tiziani S, Manzoor S, Southam A, Byrne J, Bishop L, He S, Arvanitis T, Günther U, Viant M: Birmingham Metabolite Library: a publicly accessible database of 1-D 1H and 2-D 1H J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics. 2012, 8: 8-18. 10.1007/s11306-011-0347-7.View ArticleGoogle Scholar
- Tulpan D, Léger S, Belliveau L, Culf A, Čuperlović-Culf M: MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures. BMC Bioinformatics. 2011, 12: 400-10.1186/1471-2105-12-400.View ArticlePubMed CentralPubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.