Summary: MetExploreViz is an open source web component that can be easily embedded in any

web site. It provides features dedicated to the visualization of metabolic networks and pathways

and thus offers a flexible solution to analyse omics data in a biochemical context.

Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license)

are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc

Chazalviel, Maxime

Cottret, Ludovic

Frainay, Clément

Gloaguen, Yoann

Jourdan, Fabien

Merlet, Benjamin

Poupin, Nathalie

Vinson, Florence

English

Abstract
               
                  Summary
                  MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context.
               
               
                  Availability and implementation
                  Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/
               </jats:sec

Maxime Chazalviel

Clément Frainay

Nathalie Poupin

Florence Vinson

Benjamin Merlet

Yoann Gloaguen

Ludovic Cottret

Fabien Jourdan

Crossref

Bioinformatics

MetExploreViz: web component for interactive metabolic network visualization

Enlighten: Publications

Systems biology
MetExploreViz: web component for interactive
metabolic network visualization
Maxime Chazalviel1,2, Cle´ment Frainay1, Nathalie Poupin1,
Florence Vinson1, Benjamin Merlet1, Yoann Gloaguen3, Ludovic Cottret4
and Fabien Jourdan1,*
1Toxalim, Universite´ de Toulouse, INRA, Universite´ de Toulouse 3 Paul Sabatier, Toulouse, France, 2MedDay
Pharmaceuticals, Paris, France, 3Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University
of Glasgow, Wolfson Wohl Cancer Research Centre, Gascube Campus, Bearsden G61 1QH, UK and 4LIPM,
Universite´ de Toulouse, INRA, CNRS, Castanet-Tolosan, France
*To whom correspondence should be addressed
Associate editor: Alfonso Valencia
Received on April 12, 2017; revised on August 4, 2017; editorial decision on September 13, 2017; accepted on September 14, 2017
Abstract
Summary: MetExploreViz is an open source web component that can be easily embedded in any
web site. It provides features dedicated to the visualization of metabolic networks and pathways
and thus offers a flexible solution to analyse omics data in a biochemical context.
Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license)
are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/
Contact: contact-metexplore@inra.fr
1 Introduction
Visualization is a much-needed approach in the era of untargeted
omics since it enables discovering new insights in large molecular
data corpus (Callaway, 2016). For metabolic studies, challenge res-
ides in creating visual representations of all, or part of, the thou-
sands of reactions and metabolites constituting genome scale
metabolic networks.
Visualizing metabolic networks necessitates specific graphical features
that are not necessarily available in generic network visualization tools
(Bastian et al., 2009; Cline et al., 2007) or web based tools (Franz et al.,
2016). Resources are available to perform omics data mapping on meta-
bolic pathways (Caspi et al., 2016; Kanehisa et al., 2014) or on overview
of metabolism (Noronha et al., 2017), but they mostly rely on manually
drawn maps which cannot integrate network updates or be used for new
metabolic reconstructions (Karp et al., 2016). Automatic drawing is thus
needed to fit current growing research activity in the field.
Current trend in web development is oriented toward modular
architectures allowing assembling specialized web components. In this
context, we designed MetExploreViz as an easy to install open source
web component that can quickly and easily be embedded in any web-
site. Only few lines of code are required to add MetExploreViz to any
web page (see installation section at http://metexplore.toulouse.inra.fr/
metexploreViz/doc/).
2 Metabolic network visualization features
In order to make MetExploreViz a versatile solution, it is possible to
import networks in JSON format. These networks can for instance
be retrieved by using MetExplore web services (Cottret et al., 2010).
Drawing is achieved using an animated version of force directed lay-
out implemented in D3.js library upon which MetExploreViz is
built. It is also possible to manually edit the representation and then
save the drawing in JSON format for further reuse.
An issue when drawing metabolic networks is the presence of
highly connected small molecules like water or CO2 which are not
necessarily of biochemical interest for data interpretation. To facili-
tate the visual inspection, these side compounds can be duplicated
or deleted in MetExploreViz (Fig. 1b and c).
Each element of a metabolic network can be involved in one or
more metabolic pathways and be present in one or several cellular
compartments. MetExploreViz offers an original feature to visualize
this information using convex hulls (Fig. 1d). Since the force directed
algorithm may lead to overlapping hulls, we implemented an algo-
rithm to take these sub-structures into account during the drawing.
MetExploreViz purpose is the visualization of omics data on reac-
tions and metabolites. It is thus possible to import data and map them
in a discrete or continuous way using predefined or selected colors (see
Fig. 1e and f).
VC The Author 2017. Published by Oxford University Press. 312
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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Bioinformatics, 34(2), 2018, 312–313
doi: 10.1093/bioinformatics/btx588
Advance Access Publication Date: 15 September 2017
Applications Note
Downloaded from https://academic.oup.com/bioinformatics/article-abstract/34/2/312/4158790
by University of Glasgow user
on 03 April 2018
In order to focus on a core sub-network (see Fig. 1g), MetExploreViz
implements a sub-network extraction method computing the union of
lightest paths between each pairs of mapped (or selected) nodes in the net-
work (Frainay and Jourdan, 2017). Lightest paths aim at avoiding side
compounds by weighting nodes according to their degree defined as the
number of in and out connections. Classical image exports are available
and completed with an SVG export well suited for online representations.
Since MetExploreViz focuses on metabolic networks, it does not pro-
vide all the features available in generic network analysis software suites
like Cytoscape or Gephi. To allow compatibility with these tools, it is
possible in MetExploreViz to export networks in gml and dot formats.
MetExploreViz is provided as an open source web component
that only requires few lines of code to be embedded in a web site.
3 Discussion and application
MetExploreViz and metabolic pathways oriented representations like
KEGG are complementary visualization solutions. MetExploreViz is
focused on integrative and flexible network import and representation,
while pathway oriented tools are guiding data interpretation toward a
priori defined metabolic functions.
MetExploreViz is embedded in MetExplore web server (Cottret et al.,
2010). But its component oriented architecture allows it to be used in
various web servers. For instance, it is available in MetaboLights data re-
pository for metabolomics data sets (Kale et al., 2016). We are also cur-
rently working on the integration of MetExploreViz in galaxy pipelines
(Giacomoni et al., 2015). MetExploreViz provides complementary fea-
tures to Escher (King et al., 2015) like sub-network extraction and is not
focused on network curation, making it a lightweight component.
The tool has been used for several projects as shown on Figure 1g
where metabolomics data from (Madalinski et al., 2008) are represented.
The extracted sub-network shown on the figure raised similar conclusions
as the ones described in Milreu et al. (2014). Tutorial available at http://
metexplore.toulouse.inra.fr/metexploreViz/doc/ allows repeating this
analysis.
Acknowledgements
Authors would like to thank EBI team and all the users who provided regular
feedbacks. We are grateful to the genotoul bioinformatics platform Toulouse
Midi-Pyrenees for providing computing and storage resources.
Funding
This work was supported by the French Ministry of Research and National
Research Agency as part of the French MetaboHUB [Grant ANR-INBS-0010];
Wellcome Trust [Grant 105614/Z/14/Z]; and PhenoMeNal project, European
Commission’s Horizon 2020 Program [Grant Agreement Number 654241].
References
Bastian,M. et al. (2009) Gephi: an open source software for exploring and
manipulating networks. In: Int. AAAI Conf. WEBLOGS Soc. MEDIA.
Callaway,E. (2016) The visualizations transforming biology.Nature, 535, 187–188.
Caspi,R. et al. (2016) The MetaCyc database of metabolic pathways and en-
zymes and the BioCyc collection of pathway/genome databases. Nucleic
Acids Res., 44, D471–D480.
Cline,M.S. et al. (2007) Integration of biological networks and gene expres-
sion data using Cytoscape. Nat. Protoc., 2, 2366–2382.
Cottret,L. et al. (2010) MetExplore: a web server to link metabolomic experiments
and genome-scale metabolic networks.Nucleic Acids Res., 38, W132–W137.
Frainay,C., and Jourdan,F. (2017) Computational methods to identify metabolic
sub-networks based on metabolomic profiles. Brief. Bioinform, 18, 43–56.
Franz,M. et al. (2016) Cytoscape.js: a graph theory library for visualisation
and analysis. Bioinformatics, 32, 309–311.
Giacomoni,F. et al. (2015) Workflow4Metabolomics: a collaborative research in-
frastructure for computational metabolomics.Bioinformatics, 31, 1493–1495.
Kale,N.S. et al. (2016) MetaboLights: an open-access database repository for
metabolomics data. Curr. Protoc. Bioinformatics, 53, 14.13.1–18.
Kanehisa,M. et al. (2014) Data, information, knowledge and principle: back
to metabolism in KEGG. Nucleic Acids Res., 42, D199–D205.
Karp,P.D. et al. (2016) Pathway Tools version 19.0 update: software for path-
way/genome informatics and systems biology. Brief. Bioinform., 17, 877–890.
King,Z.A. et al. (2015) Escher: a web application for building, sharing, and
embedding data-rich visualizations of biological pathways. PLoS Comput.
Biol., 11, e1004321.
Madalinski,G. et al. (2008) Direct introduction of biological samples into a
LTQ-Orbitrap hybrid mass spectrometer as a tool for fast metabolome ana-
lysis. Anal. Chem., 80, 3291–3303.
Milreu,P.V. et al. (2014) Telling metabolic stories to explore metabolomics data: a
case study on the yeast response to cadmium exposure.Bioinformatics, 30, 61–70.
Noronha,A. et al. (2017) ReconMap: an interactive visualization of human
metabolism. Bioinformatics, 31, 605–607.
Thiele,I. et al. (2013) A community-driven global reconstruction of human
metabolism. Nat. Biotechnol., 31, 419–425.
Fig. 1. Main MetExploreViz features. (a) Visualization of Recon2 human metabolic network containing 7440 reactions (Thiele et al., 2013). (b) Sub-network corresponding to
glycolysis pathway in cytosol. Highly connected nodes in grey (ATP, ADP, NAD and NADH) on the left network are duplicated to create the sparser representation on the right.
(c) Zoom in view on the metabolic pathway, reversible reactions are depicted using double-headed arrows. Smaller nodes are duplicated side compounds.
(d) Compartments where glycolysis is taking place are highlighted using colored convex hulls (e.g. cytosol in top right corner). (e) Sub-network with discrete values mapped
on reactions. (f) Flux values mapped on edges around reactions. (g) KEGG network of Saccharomyces cerevisiae with metabolomics dataset from (Madalinski et al., 2008)
mapped (dark nodes). On the right the sub-network automatically obtained using the algorithm computing the union of lightest paths implemented in MetExploreViz (Color
version of this figure is available at Bioinformatics online.)
MetExploreViz 313
Downloaded from https://academic.oup.com/bioinformatics/article-abstract/34/2/312/4158790
by University of Glasgow user
on 03 April 2018


Enlighten

Systems biologyMetExploreViz: web component for interactivemetabolic network visualizationMaxime Chazalviel1,2, Cle´ment Frainay1, Nathalie Poupin1,Florence Vinson1, Benjamin Merlet1, Yoann Gloaguen3, Ludovic Cottret4and Fabien Jourdan1,*1Toxalim, Universite´ de Toulouse, INRA, Universite´ de Toulouse 3 Paul Sabatier, Toulouse, France, 2MedDayPharmaceuticals, Paris, France, 3Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, Universityof Glasgow, Wolfson Wohl Cancer Research Centre, Gascube Campus, Bearsden G61 1QH, UK and 4LIPM,Universite´ de Toulouse, INRA, CNRS, Castanet-Tolosan, France*To whom correspondence should be addressedAssociate editor: Alfonso ValenciaReceived on April 12, 2017; revised on August 4, 2017; editorial decision on September 13, 2017; accepted on September 14, 2017AbstractSummary: MetExploreViz is an open source web component that can be easily embedded in anyweb site. It provides features dedicated to the visualization of metabolic networks and pathwaysand thus offers a flexible solution to analyse omics data in a biochemical context.Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license)are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/Contact: contact-metexplore@inra.fr1 IntroductionVisualization is a much-needed approach in the era of untargetedomics since it enables discovering new insights in large moleculardata corpus (Callaway, 2016). For metabolic studies, challenge res-ides in creating visual representations of all, or part of, the thou-sands of reactions and metabolites constituting genome scalemetabolic networks.Visualizing metabolic networks necessitates specific graphical featuresthat are not necessarily available in generic network visualization tools(Bastian et al., 2009; Cline et al., 2007) or web based tools (Franz et al.,2016). Resources are available to perform omics data mapping on meta-bolic pathways (Caspi et al., 2016; Kanehisa et al., 2014) or on overviewof metabolism (Noronha et al., 2017), but they mostly rely on manuallydrawn maps which cannot integrate network updates or be used for newmetabolic reconstructions (Karp et al., 2016). Automatic drawing is thusneeded to fit current growing research activity in the field.Current trend in web development is oriented toward modulararchitectures allowing assembling specialized web components. In thiscontext, we designed MetExploreViz as an easy to install open sourceweb component that can quickly and easily be embedded in any web-site. Only few lines of code are required to add MetExploreViz to anyweb page (see installation section at http://metexplore.toulouse.inra.fr/metexploreViz/doc/).2 Metabolic network visualization featuresIn order to make MetExploreViz a versatile solution, it is possible toimport networks in JSON format. These networks can for instancebe retrieved by using MetExplore web services (Cottret et al., 2010).Drawing is achieved using an animated version of force directed lay-out implemented in D3.js library upon which MetExploreViz isbuilt. It is also possible to manually edit the representation and thensave the drawing in JSON format for further reuse.An issue when drawing metabolic networks is the presence ofhighly connected small molecules like water or CO2 which are notnecessarily of biochemical interest for data interpretation. To facili-tate the visual inspection, these side compounds can be duplicatedor deleted in MetExploreViz (Fig. 1b and c).Each element of a metabolic network can be involved in one ormore metabolic pathways and be present in one or several cellularcompartments. MetExploreViz offers an original feature to visualizethis information using convex hulls (Fig. 1d). Since the force directedalgorithm may lead to overlapping hulls, we implemented an algo-rithm to take these sub-structures into account during the drawing.MetExploreViz purpose is the visualization of omics data on reac-tions and metabolites. It is thus possible to import data and map themin a discrete or continuous way using predefined or selected colors (seeFig. 1e and f).VC The Author 2017. Published by Oxford University Press. 312This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 34(2), 2018, 312–313doi: 10.1093/bioinformatics/btx588Advance Access Publication Date: 15 September 2017Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article-abstract/34/2/312/4158790by University of Glasgow useron 03 April 2018In order to focus on a core sub-network (see Fig. 1g), MetExploreVizimplements a sub-network extraction method computing the union oflightest paths between each pairs of mapped (or selected) nodes in the net-work (Frainay and Jourdan, 2017). Lightest paths aim at avoiding sidecompounds by weighting nodes according to their degree defined as thenumber of in and out connections. Classical image exports are availableand completed with an SVG export well suited for online representations.Since MetExploreViz focuses on metabolic networks, it does not pro-vide all the features available in generic network analysis software suiteslike Cytoscape or Gephi. To allow compatibility with these tools, it ispossible in MetExploreViz to export networks in gml and dot formats.MetExploreViz is provided as an open source web componentthat only requires few lines of code to be embedded in a web site.3 Discussion and applicationMetExploreViz and metabolic pathways oriented representations likeKEGG are complementary visualization solutions. MetExploreViz isfocused on integrative and flexible network import and representation,while pathway oriented tools are guiding data interpretation toward apriori defined metabolic functions.MetExploreViz is embedded in MetExplore web server (Cottret et al.,2010). But its component oriented architecture allows it to be used invarious web servers. For instance, it is available in MetaboLights data re-pository for metabolomics data sets (Kale et al., 2016). We are also cur-rently working on the integration of MetExploreViz in galaxy pipelines(Giacomoni et al., 2015). MetExploreViz provides complementary fea-tures to Escher (King et al., 2015) like sub-network extraction and is notfocused on network curation, making it a lightweight component.The tool has been used for several projects as shown on Figure 1gwhere metabolomics data from (Madalinski et al., 2008) are represented.The extracted sub-network shown on the figure raised similar conclusionsas the ones described in Milreu et al. (2014). Tutorial available at http://metexplore.toulouse.inra.fr/metexploreViz/doc/ allows repeating thisanalysis.AcknowledgementsAuthors would like to thank EBI team and all the users who provided regularfeedbacks. We are grateful to the genotoul bioinformatics platform ToulouseMidi-Pyrenees for providing computing and storage resources.FundingThis work was supported by the French Ministry of Research and NationalResearch Agency as part of the French MetaboHUB [Grant ANR-INBS-0010];Wellcome Trust [Grant 105614/Z/14/Z]; and PhenoMeNal project, EuropeanCommission’s Horizon 2020 Program [Grant Agreement Number 654241].ReferencesBastian,M. et al. (2009) Gephi: an open source software for exploring andmanipulating networks. In: Int. AAAI Conf. WEBLOGS Soc. MEDIA.Callaway,E. (2016) The visualizations transforming biology.Nature, 535, 187–188.Caspi,R. et al. (2016) The MetaCyc database of metabolic pathways and en-zymes and the BioCyc collection of pathway/genome databases. NucleicAcids Res., 44, D471–D480.Cline,M.S. et al. (2007) Integration of biological networks and gene expres-sion data using Cytoscape. Nat. Protoc., 2, 2366–2382.Cottret,L. et al. (2010) MetExplore: a web server to link metabolomic experimentsand genome-scale metabolic networks.Nucleic Acids Res., 38, W132–W137.Frainay,C., and Jourdan,F. (2017) Computational methods to identify metabolicsub-networks based on metabolomic profiles. Brief. Bioinform, 18, 43–56.Franz,M. et al. (2016) Cytoscape.js: a graph theory library for visualisationand analysis. Bioinformatics, 32, 309–311.Giacomoni,F. et al. (2015) Workflow4Metabolomics: a collaborative research in-frastructure for computational metabolomics.Bioinformatics, 31, 1493–1495.Kale,N.S. et al. (2016) MetaboLights: an open-access database repository formetabolomics data. Curr. Protoc. Bioinformatics, 53, 14.13.1–18.Kanehisa,M. et al. (2014) Data, information, knowledge and principle: backto metabolism in KEGG. Nucleic Acids Res., 42, D199–D205.Karp,P.D. et al. (2016) Pathway Tools version 19.0 update: software for path-way/genome informatics and systems biology. Brief. Bioinform., 17, 877–890.King,Z.A. et al. (2015) Escher: a web application for building, sharing, andembedding data-rich visualizations of biological pathways. PLoS Comput.Biol., 11, e1004321.Madalinski,G. et al. (2008) Direct introduction of biological samples into aLTQ-Orbitrap hybrid mass spectrometer as a tool for fast metabolome ana-lysis. Anal. Chem., 80, 3291–3303.Milreu,P.V. et al. (2014) Telling metabolic stories to explore metabolomics data: acase study on the yeast response to cadmium exposure.Bioinformatics, 30, 61–70.Noronha,A. et al. (2017) ReconMap: an interactive visualization of humanmetabolism. Bioinformatics, 31, 605–607.Thiele,I. et al. (2013) A community-driven global reconstruction of humanmetabolism. Nat. Biotechnol., 31, 419–425.Fig. 1. Main MetExploreViz features. (a) Visualization of Recon2 human metabolic network containing 7440 reactions (Thiele et al., 2013). (b) Sub-network corresponding toglycolysis pathway in cytosol. Highly connected nodes in grey (ATP, ADP, NAD and NADH) on the left network are duplicated to create the sparser representation on the right.(c) Zoom in view on the metabolic pathway, reversible reactions are depicted using double-headed arrows. Smaller nodes are duplicated side compounds.(d) Compartments where glycolysis is taking place are highlighted using colored convex hulls (e.g. cytosol in top right corner). (e) Sub-network with discrete values mappedon reactions. (f) Flux values mapped on edges around reactions. (g) KEGG network of Saccharomyces cerevisiae with metabolomics dataset from (Madalinski et al., 2008)mapped (dark nodes). On the right the sub-network automatically obtained using the algorithm computing the union of lightest paths implemented in MetExploreViz (Colorversion of this figure is available at Bioinformatics online.)MetExploreViz 313Downloaded from https://academic.oup.com/bioinformatics/article-abstract/34/2/312/4158790by University of Glasgow useron 03 April 2018

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MetExploreViz: web component for interactive metabolic network visualization

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