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Analysis with biological pathways:
 

Analysis with biological pathways:

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Presentation pathway extensions using knowledge integration and network approaches presented at the Systems Biology Institute in Luxembourg on November 28 2012.

Presentation pathway extensions using knowledge integration and network approaches presented at the Systems Biology Institute in Luxembourg on November 28 2012.

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  • 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,
  • 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.
  • Showing the concept. Integrating flux predictions from modelling (of course that could also be real fluxomics data)
  • 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
  • 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

Analysis with biological pathways: Analysis with biological pathways: Presentation Transcript

  • Analysis with biological pathways:using biological network approaches fordynamic pathway extension withregulatory information.Chris EveloDepartment ofBioinformatics - BiGCaTMaastricht UniversityThe Netherlands
  • 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
  • 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
  • Faculty of Health, Medicine and Life Sciences
  • 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
  • Download Pathways Web services SPARQL endpoint
  • How to dodata visualization?
  • Connect to Genome Databases
  • Backpages link to databasesFaculty of Health, Medicine and Life Sciences
  • You could do this for gene listsFaculty of Health, Medicine and Life Sciences
  • BridgeDB: Abstraction Layer class IDMapperRdb relational database interface IDMapper class IDMapperFile tab-delimited text class IDMapperBiomart web serviceThe BridgeDb Framework: Standardized Access to Gene, Protein and Metabolite IdentifierMapping 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.
  • Pathway Loom, weaving pathwaysFaculty of Health, Medicine and Life Sciences
  • 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 ChemistryNormalisation & 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
  • Extending pathways, how to do it?Faculty of Health, Medicine and Life Sciences
  • Network approaches to extend pathwaysE.g. most pathways don’t have miRNA’s
  • Adding miRNA’s clutters
  • 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
  • Or consider pathway as a networkFaculty of Health, Medicine and Life Sciences
  • GPML Cytoscape Pluginhttp://www.pathvisio.org/wiki/Cytoscape_plugin
  • Cytoscape visualization used to groupPPS1LiverAll pathwaysPathways with high z-scoregrouped together.Explains why there arerelatively few significantgenes, but many pathwayswith 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
  • Explore pathway interactionsThomas 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: 127Aug. http://dx.doi.org/doi:10.1186/1752-0509-5-127
  • What we usedNon-redundant shortest paths in a weightedgraph.1. A set of pathways2. An interaction network3. Weight value for all edges = experimental expression of connected genes.
  • Pathway interactions and what causes them
  • An indirect interaction between the Axon Guidance and Insulin Signaling pathways in the network forthe comparison between HF and LF diet at t = 0. Left: Network representation of the identified pathbetween the two pathways, consisting of three proteins Gsk3b, Sgk3 and Tsc1. Right: The location of theseproteins in the KEGG pathway diagrams. The newly found indirect interactions have been added in red.
  • Pathway interactions anddetailed network visualizationfor the interactions with threeapoptosis related pathways forthe comparison between HF andLF diet at t = 0. A: Subgraph of thepathway interaction network, basedon incoming interactions to threestress response and apoptosispathways with the highest in-degree. Pathway nodes with a thickborder are significantly enriched (p< 0.05) with differentially expressedgenes. B: The protein interactionsthat compose the interactionsbetween the three apoptosisrelated pathways and theirneighbors in the subgraph asshown in box A (see inset, includedinteractions are colored orange).Protein nodes have a thick borderwhen their encoding genes aresignificantly differentially expressed(q < 0.05).
  • We tried to make it easier withThe CyTargetLinker Cytoscape PluginExtending pathways on the fly. Provided databases with the plugin: • miRNAs with targets • Transciption Factors with targets • Drug – Target Interactions • ENCODE derived databases Extend with your own.
  • 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.
  • MiRNAs of InterestmiRNA target information from mirTarBase
  • miRNAs associated with colorectal cancerextended with validated target genes
  • human ErbB signaling pathway extendedwith validated microRNA regulation
  • Visualizing fluxes on metabolic pathways Anwesha DuttaVisualizing fluxes on metabolic pathways 33
  • DataMetabolite FluxVisualizing fluxes on metabolic pathways 34
  • File format HMDB http://www.pathvisio.org/wiki/DatabasesMapps Metabolic Data ReactomeVisualizing fluxes on metabolic pathways 35
  • Data visualized on pathwayVisualizing fluxes on metabolic pathways 36
  • Integrating it allVisualizing fluxes, data and annotation
  • SNP pathways look like this….Faculty of Health, Medicine and Life Sciences
  • Gene/Protein Y Metabolite X TF RS00005 RS00002 Gene/Protein Z RS00001 RS00003 RS00004 mi999 Metabolite YFunctionalize SNPs Unkown function (attribute to gene) Changing protein functionality (coding) In miRNA binding site Changing protein interactions (coding) In TF binding site
  • 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.
  • Thanks! Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant.