1. Exploring PPI networks using Cytoscape
Piet todo; add nice picture of one of the datasets
EMBO Practical Course Session 8
Nadezhda Doncheva and Piet Molenaar

2. Course Outline Network New hypotheses 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
Piet: Add image here
New hypotheses
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3. Instructor Introductions
Nadezhda Doncheva
Max Planck Institute for Informatics,
Saarbrücken, Germany
Piet Molenaar
AMC Oncogenomics,
Amsterdam, The Netherlands
Network visualization and analysis using Cytoscape
Developing Cytoscape plugins in Java
Member of Cytoscape dev-team
Graph analysis using Cytoscape
Developed Cytoscape core plugin
Aidan Budd
Computational Biologist,
Gibson Team,
EMBL Heidelberg
Course coordinator/organizer
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4. Schedule Timeslot Course item 09:00-10:30 Introduction
Networks and graph theory
Cytoscape workflow
Tutorial session 1
Focus: network generation
10:30-11:00
Coffee break
11:00-12:30
Tutorial session 2
Focus: network annotation and visualization
12:30-14:00
Lunch
14:00-15:30
Tutorial session 3
Focus: network analysis
15:30-16:00
Tea break
17:30-18:30
Afternoon session; Additional networking ;-)
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5. 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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6. 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 types
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7. 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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8. 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
”What do you want to do with your network?”
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9. Network visualization
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
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10. 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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11. Common network analysis tasks
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.
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12. Networks as graphs
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!
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13. Node degree and shortest path
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.
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14. Small-world networks
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)
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15. Scale-free networks Many biological networks are scale-free.
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.
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16. Scale-free vs. random networks
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, )
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17. Clustering coefficient
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.
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18. 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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19. 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 1. 2. 3. 4.
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20. Network organization The 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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21. Available software tools
Cytoscape
BioLayout Express3D
VisANT
Ondex
Pajek
Ingenuity Pathway Analysis
Pathway Studio
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22. Why Cytoscape? www.cytoscape.org 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
Piet show citations in Pubmed?
1040 articles citing the first Cytoscape publication:
+ 74 citing the most recent one:
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23. Network analysis using Cytoscape
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24. Cytoscape extended functionality
Cytoscape extends its functionality with plugins or apps
Developed by third parties
Listed at
Usually available through the Plugin Manager
Can be downloaded from the plugins’s websites
Cover many diverse areas of application
Show this live on the website
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25. A typical Cytoscape workflow
Load networks
Load attributes
Analyze and visualize networks
Prepare for publication
Cline, et al. ”Integration of biological networks and gene expression data using Cytoscape”, Nature Protocols, 2, (2007).
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26. Some useful Cytoscape links
Download:
Tutorials:
Cytoscape Mailing lists:
Plugins/Apps:
Documentation:
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27. On to the first Tutorial session
Unless any questions ???
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