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Overview

Overview. Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and complexes Domains and conclusion. Biological Network Data (Getting external stuff). Lecture Cytoscape plugins

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Overview

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  1. Overview • Introduction • Biological network data • Text mining • Gene Ontology • Expression data basics • Expression, text mining, and GO • Modules and complexes • Domains and conclusion

  2. Biological Network Data (Getting external stuff) • Lecture • Cytoscape plugins • Protein interactions: types and measurement • Protein association: text mining and coexpression • Public data repositories • Hands-on • Installing Cytoscape plugins • Filters • A few external data resources

  3. Cytoscape Plugins available for…. • Gene Ontology analysis • Domain-level protein network analysis • Interface to the Oracle spatial network data model • Shortest-Path graph analysis algorithms

  4. Interactions • Protein-protein interactions • Protein-DNA interactions • Associations (co-expression, text mining, etc).

  5. Protein-protein interactions Source: http://www.biocarta.com/pathfiles/h_caspasePathway.asp

  6. Measuring protein-protein interactions: • Yeast Two-Hybrid Source: http://www.bioteach.ubc.ca/

  7. Measuring protein-protein interactions • Co-immunoprecipitation (Co-IP) Courtesy of Rhoded Sharan, Tel Aviv University

  8. Key points on protein interactions • High false positive rate • High false negative rate • Currently, not much overlap between published interaction datasets • Most confidence given to observed interactions with other supporting evidence.

  9. Protein-DNA interactions From: Molecular Biology of the Cell, Alberts et al., 2002

  10. Measuring Protein-DNA Interactions • ChIP-on-chip From: http://www.chiponchip.org/

  11. Key points on protein-DNA interactions • There has not been much data historically. • With new technology, that is changing rapidly. • The technology is still immature, and data interpretation should be done cautiously.

  12. Text mining Courtesy of Gary Bader, Memorial Sloan Kettering Cancer Center

  13. Conserved co-expression networks From: Genome Biology 2004, 5:R100

  14. Genetic Interactions From: Nature Biotechnology23, 561 - 566 (2005)

  15. Key points on association data • An association does not imply an interaction. • Compared to protein interaction data • Higher false positive rate • Often better coverage, lower false negative rate

  16. Always remember: interactions are context-dependent! From: de Lichtenberg et al., Science. 2005 Feb 4;307(5710):724-7

  17. Also: Metabolic pathways

  18. Public data repositories • Protein-protein interaction data • BIND, DIP, MINT, MIPS, InACT, … • Protein-DNA interaction data • BIND, Transfac, … • Metabolic pathway data • BioCyc, KEGG, WIT, … • Text-mining, coexpression • Pre-BIND, Tmm, …

  19. Pathway data exchange formats: • BioPAX (supported by Cytoscape) • PSI-MI (supported by Cytoscape) • Hundreds of other formats specific to each pathway data repository (not generally supported by Cytoscape)

  20. Hands-on session • Installing Cytoscape plugins • Getting external data • Merging networks • Using filters

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