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Understanding Biological Networks: Data Integration and Analysis Tools

Explore biological network data, text mining, gene ontology, and expression basics. Delve into modules, complexes, and domains, with hands-on sessions on Cytoscape plugins and external data resources. Learn about protein interactions and associations like coexpression and text mining. Discover key points on protein-protein and protein-DNA interactions, association data, and genetic interactions. Access public data repositories for pathway data and explore data exchange formats. Enhance your understanding of network analysis tools with practical sessions.

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Understanding Biological Networks: Data Integration and Analysis Tools

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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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