Analysis with biological pathways:

using biological network approaches for dynamic pathway extension with regulatory information.

Chris EveloDepartment of Bioinformatics - BiGCaTMaastricht UniversityThe Netherlands

PathVisio

• Data modeling and visualization on biological pathways• Uses gene expression, proteomics and metabolomics data • Can identify significantly changed processes

www.pathvisio.org

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

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Understandinggenomics

ExampleWikiPathways 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

ExamplePathVisio result

Showing proteomics and transcriptomics results on the glycolysis pathway in mice liverafter starvation.

[Data from Kaatje Lenaerts and Milka Sokolovic, analysis by Martijn van Iersel]

Download PathwaysWeb services

SPARQL endpoint

How to do data visualization?

Connect to Genome Databases

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Backpages link to databases

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You could do this for gene lists

BridgeDB: Abstraction Layer

interfaceIDMapper

classIDMapperRdb

relational database

class IDMapperFile

tab-delimited text

classIDMapperBiomart

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.

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Pathway Loom, weaving pathways

RDF 3

Nanopub

Data 1

RDF 1

Descriptor

Data 2

RDF 2

Descriptor

Data 3

Descriptor

RDF 4

Nanopub

Data 4

Descriptor

RDF Data Cache

Semantic Data Workflow Engine

SparqlWeb Service API WebServices

OPS GUIOPS FrameworkArchitecture. Dec 2011

App Framework

Identity & Vocabulary

Management

Note: Things may change!

Public Vocabularies

Chemistry Normalisation &

Registration

OPS Data Model

Feed in WikiPathwaysrelationships, use BioPAXto create the RDF

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Extending pathways, how to do it?

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

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Or consider pathway as a network

GPML Cytoscape Pluginhttp://www.pathvisio.org/wiki/Cytoscape_plugin

PPS1Liver

All pathways

Pathways with high z-score grouped together.

Explains why there are relatively few significant genes, but many pathways with high z-score.

Cytoscape visualization used to group

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. e11525http://dx.doi.org/doi:10.1371/journal.pone.0011525

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

What we used

Non-redundant shortest paths in a weighted graph.

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 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.

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).

We tried to make it easier with

The 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.

Collection of miRNA-target gene interactions in the miRTarBase database with 1,715 genes,286 miRNAs and 2,817 interactions.

miRTarBase as a target interaction network

MiRNAs of Interest

miRNA target information from mirTarBase

miRNAs associated with colorectal cancer extended with validated target genes

human ErbB signaling pathway extended with validated microRNA regulation

Visualizing fluxes on metabolic pathways 33

Visualizing fluxes on metabolic pathways

Anwesha Dutta

Visualizing fluxes on metabolic pathways 34

Data

Metabolite

Flux

Visualizing fluxes on metabolic pathways 35

File format

Metabolic Data

Reactome

HMDB http://www.pathvisio.org/wiki/DatabasesMapps

Visualizing fluxes on metabolic pathways 36

Data visualized on pathway

Integrating it allVisualizing fluxes, data and annotation

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SNP pathways look like this….

Gene/Protein Z

Metabolite X

Metabolite Y

mi999

TF

RS00001

RS00003

RS00002

RS00004

Functionalize SNPsUnkown function (attribute to gene)

In miRNA binding site

In TF binding site

Changing protein functionality (coding)

Gene/Protein Y

RS00005

Changing protein interactions (coding)

Thanks!

Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant and NIH.

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)

Thanks!

Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant.