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

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 4/21/2017

Instructor Introductions Nadezhda Doncheva Max Planck Institute for Informatics, Saarbrücken, Germany http://www.mpi-inf.mpg.de/departments/d3 Piet Molenaar AMC Oncogenomics, Amsterdam, The Netherlands piet.amc@gmail.com http://humangenetics-amc.nl/ 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 http://www.embl.de/~budd/ Course coordinator/organizer 4/21/2017

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 ;-) 4/21/2017

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 4/21/2017

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 4/21/2017

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 4/21/2017

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?” 4/21/2017

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 4/21/2017

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 4/21/2017

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. 4/21/2017

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! 4/21/2017

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. 4/21/2017

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) 4/21/2017

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. 4/21/2017

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, 2001) 4/21/2017

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. 4/21/2017

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 4/21/2017

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. 4/21/2017

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 4/21/2017

Available software tools 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/ 4/21/2017

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: http://www.ncbi.nlm.nih.gov/pubmed?linkname=pubmed_pubmed_citedin&from_uid=14597658 + 74 citing the most recent one: http://www.ncbi.nlm.nih.gov/pubmed?linkname=pubmed_pubmed_citedin&from_uid=21149340 4/21/2017

Network analysis using Cytoscape 4/21/2017

Cytoscape extended functionality 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 Show this live on the website 4/21/2017

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, 2366-2382 (2007). 4/21/2017

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 Show this live on the website 4/21/2017

On to the first Tutorial session Unless any questions ??? 4/21/2017