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Using biological network approaches for dynamic extension of micronutrient related pathways with regulatory information.
1. Using biological network approaches for
dynamic extension of micronutrient related
pathways with regulatory information.
Chris Evelo
Department of
Bioinformatics - BiGCaT
Maastricht University
The Netherlands
2. You just saw pathways, right?
• Folate (Lynn Bailey, BOND)
• Riboflavin (Michael Fenech)
• Zinc (Guiditta Perozzi)
• Vitamine D (Lucia Regina Ribeiro)
• Some from Ben and I bet from Carsten
Faculty of Health, Medicine and Life Sciences
3. PathVisio
www.pathvisio.org
• Data modeling and visualization on biological pathways
• Uses gene expression, proteomics and metabolomics data
• Can identify significantly changed processes
Martijn P van Iersel, Thomas Kelder, Alexander R Pico, Kristina Hanspers, Susan Coort, Bruce R Conklin, Chris
Evelo (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9: 399
4. Understanding
genomics
Example
WikiPathways Pathway
Pathway on glycolysis.
Using modern systems
biology (MIM) annotation.
And genes and metabolites
connected to major
databases.
Faculty of Health, Medicine and Life Sciences
7. adding data =
adding colour
Example
PathVisio result
Showing proteomics
and transcriptomics
results on the glycolysis
pathway in mice liver
after starvation.
[Data from Kaatje
Lenaerts and Milka
Sokolovic, analysis by
Martijn van Iersel]
Faculty of Health, Medicine and Life Sciences
8. What do we really need? Well…
Faculty of Health, Medicine and Life Sciences
9. WikiPathways
• Public resource for biological pathways
• Anyone can contribute and curate
• More up-to-date representation of
biological knowledge
WikiPathways: building research communities on biological pathways. Thomas Kelder, Martijn P
van Iersel, Kristina Hanspers, Martina Kutmon, Bruce R Conklin, Chris T Evelo, Alexander R Pico.
Nucleic Acids Res 2012: 40(Database issue);D1301-7 http://dx.doi.org/10.1093/nar/gkr1074
Commentaries:
Big data: Wikiomics. Mitch Waldrop. Nature 2008: 455, 22-25
We the curators. Allison Doerr. Nature Methods 2008: 5, 754–755
No rest for the bio-wikis. Ewen Callaway. Nature 2010: 468, 359-360
14. Backpages link to databases
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15. You could do this for gene tables!
Faculty of Health, Medicine and Life Sciences
16. BridgeDB: Abstraction Layer
class
IDMapperRdb
relational database
interface
IDMapper class
IDMapperFile
tab-delimited text
class
IDMapperBiomart
web service
The BridgeDb Framework: Standardized Access to Gene, Protein and Metabolite Identifier
Mapping Services. Martijn P van Iersel, Alexander R Pico, Thomas Kelder, Jianjiong Gao, Isaac Ho,
Kristina Hanspers, Bruce R Conklin, Chris T Evelo. BMC Bioinformatics 2010, 11: 5.
18. OPS Framework
OPS GUI Architecture. Dec 2011
App
Framework
Web Service API Sparql Web
Services
OPS Data Model
Identity &
Vocabulary
Management Semantic Data Workflow Engine
RDF Data Cache
Chemistry
Normalisation &
Registration Descriptor Descriptor
Descriptor Descriptor Nanopub Nanopub
Feed in WikiPathways
RDF 1
relationships, use BioPAX RDF 2 RDF 3 RDF 4
to create the RDF
Public
Vocabularies Data 1 Data 2 Data 3 Data 4
23. PathVisio RI plugin provides backpage info
microRNAs in pathway analysis. The Regulatory Interaction plugin offers a suitable middle-ground between not including any
miRNAs in pathways, which misses this regulatory information, and including all validated miRNA-target interactions, which
clutters the pathway. After loading interaction file(s), selecting a pathway element shows the interaction partners of this
element and their expressions in a side panel. This allows for the detection of potential active regulatory mechanisms in the
study at hand.
http://www.bigcat.unimaas.nl/wiki/images/f/f6/VanHelden-poster-nbic2012.pdf
24. Or consider pathway as a network
Faculty of Health, Medicine and Life Sciences
26. Cytoscape visualization used to group
PPS1
Liver
All pathways
Pathways with high z-score
grouped together.
Explains why there are
relatively few significant
genes, but many pathways
with high z-score.
Robert Caesar et al (2010) A combined transcriptomics and lipidomics analysis of subcutaneous,
epididymal and mesenteric adipose tissue reveals marked functional differences. PLoS One 5: 7. e11525
http://dx.doi.org/doi:10.1371/journal.pone.0011525
27. Explore pathway interactions
Thomas Kelder, Lars Eijssen, Robert Kleemann, Marjan van Erk, Teake Kooistra, Chris Evelo
(2011) Exploring pathway interactions in insulin resistant mouse liver BMC Systems Biology 5: 127
Aug. http://dx.doi.org/doi:10.1186/1752-0509-5-127
28. What we used
Non-redundant shortest paths in a weighted
graph.
1. A set of pathways
2. An interaction network
3. Weight value for all edges
= experimental expression of connected
genes.
30. An indirect interaction between the Axon Guidance and Insulin Signaling pathways in the network for
the comparison between HF and LF diet at t = 0. Left: Network representation of the identified path
between the two pathways, consisting of three proteins Gsk3b, Sgk3 and Tsc1. Right: The location of these
proteins in the KEGG pathway diagrams. The newly found indirect interactions have been added in red.
31. Pathway interactions and
detailed network visualization
for the interactions with three
apoptosis related pathways for
the comparison between HF and
LF diet at t = 0. A: Subgraph of the
pathway interaction network, based
on incoming interactions to three
stress response and apoptosis
pathways with the highest in-
degree. Pathway nodes with a thick
border are significantly enriched (p
< 0.05) with differentially expressed
genes. B: The protein interactions
that compose the interactions
between the three apoptosis
related pathways and their
neighbors in the subgraph as
shown in box A (see inset, included
interactions are colored orange).
Protein nodes have a thick border
when their encoding genes are
significantly differentially expressed
(q < 0.05).
32. We tried to make it easier with
The CyTargetLinker Cytoscape Plugin
Extending pathways on the fly.
Provided databases with the plugin:
• miRNAs with targets
• Transciption Factors with targets
• Drug – Target Interactions
Extend with your own.
33. miRTarBase as a target interaction network
Collection of miRNA-target gene interactions in the miRTarBase database with 1,715 genes,
286 miRNAs and 2,817 interactions.
37. SNP pathways look like this….
Faculty of Health, Medicine and Life Sciences
38. Gene/Protein Y
Metabolite X
TF
RS00005
RS00002
Gene/Protein Z RS00001
RS00003
RS00004
mi999
Metabolite Y
Functionalize SNPs
Unkown function (attribute to gene) Changing protein functionality (coding)
In miRNA binding site Changing protein interactions (coding)
In TF binding site
40. Data and fluxes visualized on pathway
Visualizing fluxes on metabolic pathways 40
41. Thanks!
WikiPathways team:
• Martijn van Iersel (PathVisio,
BridgeDB)
• Thomas Kelder (WikiPathways,
networks)
• Alex Pico (US team leader)
• Brice Conklin (former US team leader)
• Kristina Hanspers (US curation)
• Martina Kutmon (CyTargetLinker)
• Susan Coort (Regulatory plugins)
• Lars Eijssen (Data pipelines)
• Anwesha Dutta (Flux visualisation)
• Andra Waagmeester (LOOM)
• Egon Willighagen (Open Phacts)
Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional:
Transnational University. EU: NuGO and Microgennet,
IMI: Open Phacts + Agilent thought leader grant and
NIH.
42. Thanks!
Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional:
Transnational University. EU: NuGO and Microgennet,
IMI: Open Phacts + Agilent thought leader grant.
Notas do Editor
Probably not an iPAD, those microarrays were at least 10 years old.
A closer look at the same pathway.Note that this uses MIM notation from the MIM PathVisio plugin.In general the connections between different genes and metabolites describe the network underlying the pathway. Note that this is already quite complex since there are different ways to show what interacts with what.Graphical methods to capture this like MIM and SBGN definitely help. The result can be captures in descriptive relationships in BioPax,
Probably not an iPAD, those microarrays were at least 10 years old.
As soon as you have entered one (and only one) identifier to describe what gene product or metabolite you really mean this information is linked to many other identifiers from other databases and links to these respective pages are shown in the so called “backpage” (actually one of the pages under the tabs at the righthand side of the pathway).
As soon as you have entered one (and only one) identifier to describe what gene product or metabolite you really mean this information is linked to many other identifiers from other databases and links to these respective pages are shown in the so called “backpage” (actually one of the pages under the tabs at the righthand side of the pathway).
BridgeDB (see www.bridgedb.org and the paper mentioned on the slide) provides the mechanism needed for that identifier mapping.
There are just too many SNPs for any given gene.
An overview of the Open Phacts project that pulls in lots of information in a semantic web triple store (including information from WikiPathways RDF) and then provides that for use in other tools. In WikiPathways we use that to suggest possible pathway extensions to curators
Probably not an iPAD, those microarrays were at least 10 years old.
Introducing a problem
And a solution that isn’t really a solution. There are just too many things you could add.
The PathVisio Regulatory Interaction plugin (author Stefan van Helden) has a new approach where information is not really added to a pathway, but shown in a separate page upon request.
Probably not an iPAD, those microarrays were at least 10 years old.
The approach takes into account all data use (pathways, interactions and experimentally determined weight). Check out the original paper for details.
Example result. Pathways with stronger interaction based on gene snot present in them.
And you can do the same for relatively large sets of pathways “driving” a process like apoptosis.
CyTargetLinker is a Cytoscape plugin that can be used to extend one network with information about things targeting entities in that network from databases that are created as a network. It already provides a number of target relation databases as mentioned on the slide.
Example of a target network. (You will normally see this, it contains the information that is used to extend your source network).
You can drive it from a gene set, that isn’t even a network at the start. But when miRNAs are found to target more than one gene in the ggroup the network is created on the fly.
Or you can bootstrap the approach from an existing network. Which can be a pathway based one imported with the GPML plugin like shown here.
There are just too many SNPs for any given gene.
There are loads of bioinformatics tools out there (like Sift and Polyphen) that allow us to estimate functional effects of SNPs on coded protein (activity or protein-protein interactions), binding site for transcription factors in the DNA, or miRNA in RNA. Doing that we can decide what edges SNPs would affect (and how much in what direction). Now as soon as you do that you can use the result to strengthen SNP statistics (ie create groups that can be used for supervised types of group based GWAS analysis) or to build predictive models to estimate that specific (personal or tissue/tumor based) sets of variations would do. That provides a need to use the pathways to link experimental (genomics) data not only to the genetic variations occurring in there, but also to modeling results
Showing the concept. Integrating flux predictions from modelling (of course that could also be real fluxomics data)
Many people involved in this work. (Really many if you count associated groups like the plugin developers, pathway curators etc).Most importantSF group (Kristina Hanspers, Bruce Conklin and Alex Pico) collaborating on many things but primarily WikiPatwhaysMartijn van Iersel top left (PathVisio, BridgeDB). Thomas Kelder (top middle) (WikiPathways including webservices, pathway integration networks for nutrigenomics), Martina Kutmon (top right) (CyTargetLinker, PathVisio further development), Andra Waagmeester (second row, right) (WikiPathways RDF), Anwesha Dutta (bottom, 2nd from the left) (flux visualization), Stefan van Helden (not on the picture) for the RI PathVisio plugin
Many people involved in this work. (Really many if you count associated groups like the plugin developers, pathway curators etc).Most importantSF group (Kristina Hanspers, Bruce Conklin and Alex Pico) collaborating on many things but primarily WikiPatwhaysMartijn van Iersel top left (PathVisio, BridgeDB). Thomas Kelder (top middle) (WikiPathways including webservices, pathway integration networks for nutrigenomics), Martina Kutmon (top right) (CyTargetLinker, PathVisio further development), Andra Waagmeester (second row, right) (WikiPathways RDF), Anwesha Dutta (bottom, 2nd from the left) (flux visualization), Stefan van Helden (not on the picture) for the RI PathVisio plugin