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CSC, Dec.15-16,2005 Cytoscape Team Benno Schwikowski Trey Ideker Melissa Cline Mark Anderson Andrea Splendiadi Nerius Landys Ryan Kelley Lee Hood Chris Workman Richard Bonneau Past contributors: Rowan Christmas Nada Amin Iliana Avila-Campillo Owen Ozier Past contributors: Larissa Kamenkovich Andrew Markiel Paul Shannon Jonathan Wang Chris Sander Gary Bader Ethan Cerami Rob Sheridan Agilent Annette Adler Allan Kuchinsky Aditya Vailaya Mike Creech Logistics Four units per day 1/2 hour lecture 45 minute hands-on exercises Lecture Background for the hands-on exercises Questions and comments encouraged Hands-on exercises Mostly Cytoscape 2.2, a little Cytoscape 2.1. Teamwork encouraged Proceed at own pace Schedule Thursday am: getting started Introduction Coffee break Biological network data Lunch Thursday pm: companion resources for network analysis Text mining Coffee break Gene Ontology Leave Friday am: mRNA Expression Expression basics Coffee break Expression & text mining & GO Lunch Friday pm: advanced topics in network topology Modules and complexes Coffee break Protein domain networks, future directions, feedback. Overview Introduction What is Cytoscape? Resources for getting help Basic functionality Biological networks Motivation: most biological processes are not performed by a single, independent macromolecule Core Features • Customize network data display using visual styles • Powerful graph layout tools • Easily organize multiple networks • Easily navigate large networks • Filter the network • Plugin API Input/Output • Protein protein interactions from BIND, TRANSFAC databases • Gene functional annotations from Gene Ontology (GO) and KEGG databases • Biological models from Systems Biology Markup Language (SBML) • cPath: Cancer Pathway database • Proteomics Standards Initiative Molecular Interaction (PSI-MI) or Biopathway Exchange Language (BioPAX) formats • Oracle Spatial Network data model Cytoscape.org Cytoscape is a freely-available (open-source, java-based) bioinformatics software platform for visualizing biological networks (e.g. molecular interaction networks) and analyzing networks with gene expression profiles and other state data. Additional features are available as plugins. • jActiveModules: identify significant “active” subnetworks • Expression Correlation Network: cluster expression data • Agilent Literature Search: build networks by extracting interactions from scientific literature. • MCODE: finds clusters of highly interconnected regions in networks • cPath: query, retrieve and visualize interactions from the MSKCC Cancer Pathway database • BiNGO: determine which Gene Ontology (GO) categories are statistically over-represented in a set of genes • Motif Finder: runs a Gibbs sampling motif detector on sequences for nodes in a Cytoscape network. • CytoTalk: Interact with Cytoscape from Perl, Python, R, shell scripts or C or C++ programs. Use Case 1: Atherosclerosis King et. al., Physiol Genomics. 2005 Sep 21;23(1):103-18. Use Case 2: Hepatitis C virus infection Reiss et. al., BMC Bioinformatics 2005, 6:154 Hands-on Session 1 Navigating the Cytoscape panels Loading, editing, and saving a network Graph layout methods Setting visual properties