=Paper=
{{Paper
|id=Vol-2042/paper37
|storemode=property
|title=Visualizing Metabolomics Data in Directed Biological Networks
|pdfUrl=https://ceur-ws.org/Vol-2042/paper37.pdf
|volume=Vol-2042
|authors=Ryan Miller,Denise Slenter,Martina Kutmon,Jonathan Melius,Georg Summer,Chris Evelo,Egon Willighagen
|dblpUrl=https://dblp.org/rec/conf/swat4ls/MillerSKMSEW17
}}
==Visualizing Metabolomics Data in Directed Biological Networks==
https://ceur-ws.org/Vol-2042/paper37.pdf
Visualizing metabolomics data in directed biological networks
Denise Slenter1, Martina Kutmon1,2, Ryan Miller1, Jonathan Melius1, Georg Summer3, Chris T. Evelo1,2 and
Egon L. Willighagen1
1
Department of Bioinformatics – BiGCaT, NUTRIM Research School, Maastricht University, The Netherlands
2
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, The Netherlands
3
Department of Cardiology, Maastricht University, The Netherlands
E-mail: ryan.miller@maastrichtuniversity.nl
Background
Metabolomics data describes the state of a biological visualization functionality to investigate the identified
system at a phenotypic level. Unfortunately, not all subnetworks. The generic nature of this approach opens up
measured metabolites can be linked to metabolite identities the option to combine with other omics data sources, such
present in biological pathway models. The resulting as proteomics and transcriptomics.
sparseness makes it more complicated to use metabolomics
data in pathway and network analysis.
In 2014, Posma et al. introduced MetaboNetworks [1], a
Matlab toolbox to create sub-networks between sets of
metabolites using the reactions from the KEGG database [2]
by calculating the shortest paths between them. Such
networks overcome the metabolic data sparseness by
focussing on the paths between the metabolites of interest.
To upscale this approach, we need to be able to combine
different pathway knowledge bases and introduce detailed
directionality information, ensuring the shortest paths follow
one-directional biological cause-and-effect paths.
Materials & Methods
The presented work creates subnetworks of shortest,
directed pathways between active metabolites. First, with
the WikiPathways RDF [3], we created a directed network of
all metabolic reactions from the WikiPathways [4] and
Figure 1: Directed network of metabolic reactions.
Reactome [5] pathway knowledgebase, see Figure 1. In the
next step, we identified the location(s) of the active References
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