Different omics platforms—genomics, transcriptomics, proteomics, metabolomics and fluxomics—are generating new insights into how biological systems work at a molecular level. Although each individual omics approach provides a global view of a specific cellular process, this view is limited to only one aspect. In order to gain a comprehensive understanding of the system as a whole, researchers are faced with the challenge of merging these different types of results.
PathVisio is an open source tool for drawing and editing biological pathways and visualizing and analyzing data. In order to make multi omics data visualization more intuitive we developed new add ons for the software to enable visualization of multiple data sets that can be about data of different types. This also allows visualization of data on the lines that symbolize interactions and reactions in the pathways, essentially adding edge visualization for network biology. In this way we can for instance show results of fluxomics studies or from dynamic system biology models.
1. Multi-omics Pathway Visualization
Integration of interaction and flux data in PathVisio
Anwesha Dutta
BiGCaT - Maastricht University
Bio-IT World Europe Conference & Expo 2012, 11 October, Vienna
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2. From Data to Visualization
Data enriched
Experimentation and Pathway Pathway
data collection
Genes
Proteins
Metabolites
Interactions
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5. Pathway Databases >300 pathway resources
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6. Wikipathways www.wikipathways.org
• Wiki for biological pathways
• Free and open source resource
• Kegg & Reactome pathways also available
Share, curate
& discuss!
Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo C, Pico AR. (2011) WikiPathways: building research communities on
biological pathways. NAR doi: 10.1093/nar/gkr1074
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7. What is Data?
Quantitative information about the different elements of a
pathway, obtained experimentally or by modeling.
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8. Why visualize Data?
A picture is worth a
thousand words
~Fred Barnard
Visual representations
of experimental results
ease understanding.
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9. PathVisio www.pathvisio.org
Presenting and exploring biological pathways with PathVisio. van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin
BR, Evelo C. BMC Bioinformatics 2008, 9:399 (25 Sep 2008)
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11. Download pathways
Wikipathways
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12. Data File Format
• PathVisio accepts delimited text files
• Prepare and export from Excel
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13. Download annotation databases
www.pathvisio.org/PathVisioDownload
A database for mapping interaction identifiers
is in development and coming soon!
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14. Database identifier mapping
Your data
BridgeDB A pathway
HMDB
HMDB00122 HMDB00122
Kegg
Compound
HMDB01401 C00092
PubChem
HMDB00124 69507
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15. BridgeDb www.bridgedb.org
• Id mapping framework for bioinformatics
applications
• Translates identifiers from one system to
another
• Links out to online information for an identifier
Create your own BridgeDb derby database!
http://www.bridgedb.org/wiki/BridgeDbCreator
The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services. van Iersel MP, Pico
AR, Kelder T, Gao J, Ho I, Hanspers K, Conklin BR, Evelo CT. BMC Bioinformatics. 2010 Jan 4;11(1):5.
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17. Data Visualized
Legend:
Metabolites visualized
0 5
with a color gradient
Positive fluxes
Negative fluxes
Thickness of the arrows
correspond to the amount of
flux
Example case :Flux & metabolite data
visualized together
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18. Export Pathway
• Export to image formats
PNG
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19. Incorporation into workflows
PathVisio
PathVisioRPC
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20. PathVisio pointers
• Completely free and open source
• Fully compatible with Wikipathways
• Modular architecture, easily extendable
• Easy incorporation into pipelines
• Very active mailing list
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21. Acknowledgements
General Bioinformatics,
Reading,UK
Gladstone Institutes, SF,
USA
Max Planck Instt., TNO, Zeist, NL
Berlin,Germany
Funding
Maastricht University, NL
and many contributors around the
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22. Thank you for your time and attention!
http://www.pathvisio.com/
wiki/PathVisioTutorials
wikipathways-discuss@googlegroups.com
anwesha.dutta@maastrichtuniversity.nl
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Editor's Notes
aptly characterizes one of the main goals of visualization, namely making it possible to absorb large amounts of data quickly.