Exploring PPI networks using Cytoscape

EMBO Practical Course Session 8

Nadezhda Doncheva and Piet Molenaar

Course Outline Lectures & Labs

Protein focus

Graph context

Demo & Do it yourself use cases

Data from recent literature

Tips & Tricks

Biological questions I have a protein

Function, characteristics from known interactions

I have a list of proteins Shared features, connections

I have data Derive causal networks

Network Topology Hubs Clusters

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New hypotheses

Instructor Introductions

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Piet MolenaarAMC Oncogenomics,Amsterdam, The Netherlandspiet.amc@gmail.comhttp://humangenetics-amc.nl/

Nadezhda DonchevaMax Planck Institute for Informatics, Saarbrücken, Germanyhttp://www.mpi-inf.mpg.de/departments/d3 Network visualization and

analysis using CytoscapeDeveloping Cytoscape plugins in JavaMember of Cytoscape dev-team

Aidan BuddComputational Biologist, Gibson Team, EMBL Heidelberghttp://www.embl.de/~budd/

Course coordinator/organizer

Graph analysis using CytoscapeDeveloped Cytoscape core plugin

Schedule

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Timeslot Course item

09:00-10:30 1. Introduction• Networks and graph theory• Cytoscape workflow

2. Tutorial session 1• Focus: network generation

10:30-11:00 Coffee break

11:00-12:30 3. Tutorial session 2• Focus: network annotation and visualization

12:30-14:00 Lunch

14:00-15:30 4. Tutorial session 3• Focus: network analysis

15:30-16:00 Tea break

17:30-18:30 Afternoon session; Additional networking ;-)

Overview Introduction Part I: Introduction to molecular networks and

graph concepts What are molecular networks?

Why are they useful?

What tools are available?

Part II: Introduction to Cytoscape Network visualization

Plugins/Apps

Workflows

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Why networks? Complex systems are better described as

networks of interacting components The topology of a network characterizes the

underlying complex system (global topology parameters) and its individual components (local topology parameters)

Network topology parameters are easily compared

Useful for discovering patterns in large data sets (better than tables in Excel)

Allow the integration of multiple data types04/19/236

Biological networks Nodes can represent

proteins, genes, metabolites, etc.

Edges can be physical or functional interactions like Protein-Protein interactions

Protein-DNA interactions

Metabolic interactions

Co-expression relations

Genetic interactions

…

Important to understand what the nodes and edges mean

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Applications of network biology

Gene function prediction based on connections to sets of genes/proteins involved in same biological process

Detection of protein complexes by analyzing modularity and higher order organization (motifs, feedback loops)

Identification of disease subnetworks that are transcriptionally active in a disease

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”What do you want to do with your network?”

Network visualization

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Network layouts Force-directed: nodes repel

and edges pull

Hierarchical: for tree-like networks

Manually adjust layout

Visually interpret a network Global relationships

Dense clusters

Visual features Node and edge attributes

represent e.g. gene or interaction attributes

Map attributes to node and edge visual properties like color, shape or size

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Common network analysis tasks

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Network topology statistics such as node degree, betweenness, degree distribution of nodes, clustering coefficient, shortest path between nodes and robustness of the network to the random removal of single nodes.

Modularity refers to the identification of sub-networks of interconnected nodes that might represent molecules physically or functionally linked that work coordinately to achieve a specific function.

Motif analysis is the identification of small network patterns that are over-represented when compared with a randomized version of the same network. Discrete biological processes such as regulatory elements are often composed of such motifs.

Network alignment and comparison tools can identify similarities between networks and have been used to study evolutionary relationships between protein networks of organisms.

Networks as graphs

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Formal graph definition: A graph G is a pair of two sets V (nodes) and E (edges): G = (V, E)

Neighbors are two nodes n1 and n2 connected by an edge

Neighborhood is the set of all neighbors of node n

Connectivity kn is the size of the neighborhood of n

Degree k is the number of edges incident on n

Note that cases exist with k ≠ kn!

Node degree and shortest path

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Hub is a node with an exceptionally high degree, larger than the average node degree (see red nodes).

A shortest path between the nodes n and m is a path between n and m of minimal length.

The shortest path length, or distance, between n and m is the length of a shortest path between n and m.

The characteristic path length is the average shortest path length, the expected distance between two connected nodes.

Small-world networks

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A network is a small-world network if any two arbitrary nodes are connected by a small number of intermediate edges, i.e. the network has an average shortest path length much smaller than the number of nodes in the network (Watts, Nature, 1998).

Interaction networks have been shown to be small-world networks (Barabási, Nature Reviews in Genetics, 2004)

Scale-free networks

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Node degree distribution counts the number of nodes with degree k, for k = 0, 1, 2, …

If the node degree distribution of a network approximates a power law P(k) ~ ak-b with b < 3, the network is scale-free (Barabási, Science, 1999).

Many biological networks are scale-free.

Scale-free vs. random networks

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Random networks are homogeneous, most nodes have the same number of links)

not robust to arbitrary node failure

Scale-free networks have a number of highly connected nodes)

robust to random failure, but very sensitive to hub failures

Implications to the robustness of PPI networks (Jeong, Nature, 2001)

Clustering coefficient

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The clustering coefficient of a node n is a ratio N=M, where N is the number of edges between the neighbors of a node n, and M is the maximum number of edges that could possibly exist between the neighbors of n.

The network clustering coefficient is the average of the clustering coefficients for all nodes in the network.

Network clustering Find subsets of nodes, modules

or clusters, that satisfy some pre-defined quality measure

Benefits Finding “natural” clusters

Classifying the data

Detecting outliers

Reducing the data

Downsides Real data very rarely presents a

unique clustering

Many different models try out more than one

Several alternative solutions could exist

Interpretation of clusters

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Motifs A small connected graph with

a given number of nodes Motif frequency is the

number of different matches of a motif

Functionally relevant motifs in biological networks: Feed-forward loop (1) Bifan motif (2) Single-input motif (3) Multi-input motif (4)

Significance profiles of motifs

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1. 2.

3. 4.

Network organizationThe levels of organization of complex networks:Node degree provides information about single nodes

Three or more nodes represent a motif

Larger groups of nodes are called modules or communities

Hierarchy describes how the various structural elements are combined

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Available software tools

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Cytoscape http://cytoscape.org/

BioLayout Express3D http://www.biolayout.org/

VisANT http://visant.bu.edu/

Ondex http://www.ondex.org/

Pajek http://pajek.imfm.si/

Ingenuity Pathway Analysis http://www.ingenuity.com/products/pathways_analysis.html

Pathway Studio http://www.ariadnegenomics.com/products/pathway-studio/

Why Cytoscape?

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Visualization, Integration & Analysis Free & open source software application (LGPL license) Written in Java: can run on Windows, Mac, & Linux Developed by a consortium: UCSD, ISB, Agilent,

MSKCC, Pasteur, UCSF, Unilever, Utoronto; provide a permanent dedicated team of developers

Active community: mailing lists, annual conferences 10,000s users, 3000 downloads/month Extensible through plugins developed by third parties It is used! Lots of citations

www.cytoscape.org

Network analysis using Cytoscape

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Cytoscape extended functionality

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Cytoscape extends its functionality with plugins or apps

Developed by third parties

Listed at http://apps.cytoscape.org/

Usually available through the Plugin Manager

Can be downloaded from the plugins’s websites

Cover many diverse areas of application

A typical Cytoscape workflow

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1. Load networks

2. Load attributes

3. Analyze and visualize networks

4. Prepare for publication

Cline, et al. ”Integration of biological networks and gene expression data using Cytoscape”, Nature Protocols, 2, 2366-2382 (2007).

Some useful Cytoscape links Download:

http://www.cytoscape.org/download.html

Tutorials: http://opentutorials.cgl.ucsf.edu/index.php/Portal:Cytoscape

Cytoscape Mailing lists: http://www.cytoscape.org/community.html

Plugins/Apps: http://apps.cytoscape.org/

Documentation: http://www.cytoscape.org/documentation_users.html

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On to the first Tutorial session

Unless any questions ???

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