Omicxview: an interactive metabolic pathway visualisation tool

Miss Ariane Mora1, Mr Rowland Mosbergen2, Mr Steve Englart1, Mr Othmar Korn1, Associate Professor Mikael Boden3, Professor  Christine Wells2,4

1 Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia

2 Department of Anatomy and Neuroscience, The University of Melbourne, Melbourne, Victoria, Australia

3 School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia

4 The Walter and Eliza Hall Research Institute, Melbourne, Victoria, Australia


Omicxview is an interactive visualisation portal that enables researchers to display large metabolic datasets on well-defined Escher pathways [1]. It addresses the gap between very simple static views, such as the common approach of colouring KEGG pathways, and comprehensive networks such as Reactome, which can be so complex that the signal of interest is dwarfed by background information, and thus difficult for untutored users to navigate. Omicxview overlays experimental data onto Escher metabolic pathways, providing users with intuitive and interactive ways to explore large multi-omic datasets.


The biggest challenge faced by this project was the lack of standardised nomenclature in the Metabolomics community, leading to ambiguities when assigning a metabolite to a pathway term. Our solution was to develop a robust identification process that enables users to map their uploaded metabolites to a range of public database identifiers, including ChEBI, KEGG, BiGG, HMDB and MetaNetX.

Omicxview is an open source web application running on the NeCTAR [2] cloud. It was designed to integrate with the stem cell portal. Stemformatics is a collaboration platform for the stem cell community, designed for the rapid visualisation of multi-omics data [3].

Omicxview provides an enhanced user experience and tools for interactive visualisation of metabolic pathways. The remit includes the ability to share views and datasets and the capacity to evolve and add functionality as the underlying datasets require.

Omicxview has been developed using D3 and SVG to produce an extensible, interactive environment where the activity of metabolites of interest can be explored using a range of metrics. These include significant differences between sample types in the form of fold change or the users’ choice of statistical value. Figure 1, Figure 2 and Figure 3 are examples of such filters applied to randomly generated datasets on a range of pathways.

By providing cross-database mapping of identifiers, Omicxview supports the development of in-house standards. By providing rapid and clear visualisation of experimental data, Omicxview aids identification of experimental trends within metabolomic data series. Furthermore, collaboration is fostered between research groups by providing the opportunity to host and share private data. These attributes are representative of Stemformatics’ commitment to developing high quality, open source tools that benefit the wider stem cell community.

Stemformatics [3] developed Omicxview in collaboration with Metabolomics Australia [4], as part of the Bioplatforms Australia initiative [5].

Figure 1: Carbohydrate metabolism pathway with a randomly generated dataset to represent metabolite expression values. The metabolites have been highlighted on arbitrarily assigned p-values to display the tools provided by Omicxview.

Figure 2: Ascorbate metabolism with a randomly generated dataset to represent metabolite expression values. The graph containing the minimum sample difference has been highlighted in orange.

Figure 3: Glycolysis tricarboxylic acid cycle with a dataset containing four sample types and randomly generated metabolite expression values. Reactions between two metabolites within the dataset have been highlighted. The opaque blue outer sphere indicates that the user has uploaded statistical information for the given metabolite. The user can view the information by hovering over, or clicking on the metabolite.



  1. King, Z et al Escher: A web application for building, sharing, and embedding data-rich visualizations of biological pathways, PLOS Computational Biology 11(8): e1004321. doi:10.1371/journal.pcbi.1004321
  2. NeCTAR research cloud website accessed 12th of June 2017
  3. Wells CA et al Stemformatics: Visualisation and sharing of stem cell gene expression. Stem Cell Research, DOI
  4. Metabolomics Australia website accessed 12th of June 2017
  5. Bioplatforms Australia website accessed 12th of June 2017


Ariane Mora is completing a Bachelor of Electrical and Computer Engineering at the University of Queensland. She has recently finished her honours thesis under the supervision of Rowland Mosbergen and Associate Professor Mikael Boden, developing a metabolic pathway visualisation portal. For the past year she has worked for Stemformatics developing web based visualisation tools for stem cell researchers.

Rowland Mosbergen is the Project Manager and Lead Developer for the collaboration resource. Rowland has 17 years experience in IT while working in research, corporate financial software and small business. Rowland helped to design the Stemformatics code-base to be flexible enough to handle multi-omics datasets, and while the application is aimed at the stem cell community, the fundamentals are suitable for any community-based data-visualisation environment.

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