Overview

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Overview

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

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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

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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

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Interactions

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

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Protein-protein interactions
Source http//www.biocarta.com/pathfiles/h_caspas
ePathway.asp
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Measuring protein-protein interactions

• Yeast Two-Hybrid

Source http//www.bioteach.ubc.ca/
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Measuring protein-protein interactions

• Co-immunoprecipitation (Co-IP)

Courtesy of Rhoded Sharan, Tel Aviv University
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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.

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

• ChIP-on-chip

From http//www.chiponchip.org/
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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.

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Text mining
Courtesy of Gary Bader, Memorial Sloan Kettering
Cancer Center
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Conserved co-expression networks
From Genome Biology 2004, 5R100
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Genetic Interactions
From Nature Biotechnology 23, 561 - 566 (2005)
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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

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Always remember interactions are
context-dependent!
From de Lichtenberg et al., Science. 2005 Feb
4307(5710)724-7
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Also Metabolic pathways
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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,

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Pathway data exchange formats

1. BioPAX (supported by Cytoscape)
2. PSI-MI (supported by Cytoscape)
3. Hundreds of other formats specific to each
   pathway data repository (not generally supported
   by Cytoscape)

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Hands-on session

• Installing Cytoscape plugins
• Getting external data
• Merging networks
• Using filters