Transcript Introduction_final - Bioinfo-casl

Exploring PPI networks using
Cytoscape
EMBO Practical Course Session 8
Nadezhda Doncheva and Piet Molenaar
Course Outline
Lectures & Labs
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Protein focus
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Graph context
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Demo & Do it yourself use cases
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Data from recent literature
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Tips & Tricks
Biological questions
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I have a protein
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I have a list of proteins
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Shared features, connections
I have data
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Function, characteristics from known
interactions
Derive causal networks
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Network
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Topology
Hubs
Clusters
New hypotheses
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Instructor Introductions
Nadezhda Doncheva
Max Planck Institute for
Informatics,
Saarbrücken, Germany
http://www.mpiinf.mpg.de/departments/d3
Graph analysis using Cytoscape
Developed Cytoscape core
plugin
Piet Molenaar
AMC Oncogenomics,
Amsterdam, The Netherlands
[email protected]
http://humangenetics-amc.nl/
Network visualization and
analysis using Cytoscape
Developing Cytoscape plugins in
Java
Member of Cytoscape dev-team
Aidan Budd
Computational Biologist,
Gibson Team,
EMBL Heidelberg
http://www.embl.de/~budd/
Course coordinator/organizer
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Schedule
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 ;-)
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Overview Introduction
Part I: Introduction to molecular networks and graph
concepts
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What are molecular networks?
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Why are they useful?
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What tools are available?
Part II: Introduction to Cytoscape
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Network visualization
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Plugins/Apps
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Workflows
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Why networks?
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Complex systems are better described as networks of
interacting components
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The topology of a network characterizes the underlying
complex system (global topology parameters) and its
individual components (local topology parameters)
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Network topology parameters are easily compared
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Useful for discovering patterns in large data sets (better
than tables in Excel)
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Allow the integration of multiple data types
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Biological networks
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Nodes can represent proteins,
genes, metabolites, etc.
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Edges can be physical or
functional interactions like
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Protein-Protein interactions
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Protein-DNA interactions
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Metabolic interactions
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Co-expression relations
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Genetic interactions
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…
Important to understand what
the nodes and edges mean
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Applications of network biology
”What do you want to do with your network?”
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Gene function prediction based on connections to sets of
genes/proteins involved in same biological process
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Detection of protein complexes by analyzing modularity
and higher order organization (motifs, feedback loops)
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Identification of disease subnetworks that are
transcriptionally active in a disease
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Network visualization
Network layouts
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Force-directed: nodes repel and
edges pull
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Hierarchical: for tree-like networks
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Manually adjust layout
Visually interpret a network
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Global relationships
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Dense clusters
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Visual features
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Node and edge attributes
represent e.g. gene or
interaction attributes
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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.
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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.
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Motif analysis is the
identification of small network
patterns that are overrepresented when compared
with a randomized version of
the same network. Discrete
biological processes such as
regulatory elements are often
composed of such motifs.
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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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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)
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Neighbors are two nodes n1 and n2 connected by an edge
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Neighborhood is the set of all neighbors of node n
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Connectivity kn is the size of the neighborhood of n
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Degree k is the number of edges incident on n
 Note that cases exist with k ≠ kn!
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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.
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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).
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Interaction networks have been
shown to be small-world
networks (Barabási, Nature
Reviews in Genetics, 2004)
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Scale-free networks
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Node degree
distribution counts the
number of nodes with
degree k, for k = 0, 1, 2, …
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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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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
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Scale-free networks have a
number of highly connected
nodes)
 robust to random failure,
but very sensitive to hub
failures
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Implications to the robustness
of PPI networks (Jeong, Nature,
2001)
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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.
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The network clustering
coefficient is the average of
the clustering coefficients for all
nodes in the network.
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Network clustering
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Find subsets of nodes, modules or
clusters, that satisfy some pre-defined
quality measure
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Benefits
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Finding “natural” clusters
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Classifying the data
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Detecting outliers
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Reducing the data
Downsides
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Real data very rarely presents a unique
clustering
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Many different models  try out more
than one
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Several alternative solutions could exist
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Interpretation of clusters
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Motifs
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A small connected graph with a
given number of nodes
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Motif frequency is the number of
different matches of a motif
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Functionally relevant motifs in
biological networks:
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Feed-forward loop (1)
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Bifan motif (2)
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Single-input motif (3)
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Multi-input motif (4)
Significance profiles of motifs
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2.
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Network organization
The levels of organization of
complex networks:
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Node degree provides
information about single nodes
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Three or more nodes represent a
motif
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Larger groups of nodes are called
modules or communities
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Hierarchy describes how the
various structural elements are
combined
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Available software tools
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Cytoscape http://cytoscape.org/
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BioLayout Express3D http://www.biolayout.org/
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VisANT http://visant.bu.edu/
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Ondex http://www.ondex.org/
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Pajek http://pajek.imfm.si/
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Ingenuity Pathway Analysis
http://www.ingenuity.com/products/pathways_analysis.html
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Pathway Studio
http://www.ariadnegenomics.com/products/pathway-studio/
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Why Cytoscape?
www.cytoscape.org
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Visualization, Integration & Analysis
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Free & open source software application (LGPL license)
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Written in Java: can run on Windows, Mac, & Linux
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Developed by a consortium: UCSD, ISB, Agilent, MSKCC, Pasteur,
UCSF, Unilever, Utoronto; provide a permanent dedicated team of
developers
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Active community: mailing lists, annual conferences
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10,000s users, 3000 downloads/month
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Extensible through plugins developed by third parties
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It is used! Lots of citations
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Network analysis using Cytoscape
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Cytoscape extended functionality
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Cytoscape extends its functionality
with plugins or apps
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Developed by third parties
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Listed at http://apps.cytoscape.org/
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Usually available through the Plugin
Manager
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Can be downloaded from the
plugins’s websites
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Cover many diverse areas of
application
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A typical Cytoscape workflow
1.
Load networks
2.
Load attributes
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Analyze and visualize
networks
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Prepare for publication
Cline, et al. ”Integration of biological networks and
gene expression data using Cytoscape”, Nature
Protocols, 2, 2366-2382 (2007).
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Some useful Cytoscape links
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Download: http://www.cytoscape.org/download.html
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Tutorials:
http://opentutorials.cgl.ucsf.edu/index.php/Portal:Cytoscape
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Cytoscape Mailing lists:
http://www.cytoscape.org/community.html
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Plugins/Apps: http://apps.cytoscape.org/
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Documentation:
http://www.cytoscape.org/documentation_users.html
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On to the first Tutorial session
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Unless any questions ???
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