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Purpose
Reconstruct signal transduction pathways & protein complexes using protein-protein interactions reported
on the web
Methods
• Construct high likelihood / low noise queries• Ex: “Jak2 phophorylates Stat5”
• Query Yahoo! for every permutation of 2 proteins in a given species
• Use high likelihood joining words…
Joining Words
• Phosphorylates• Methylates• Acetylates• Activates• Deactivates• Binds to
• Inhibits• Dephosphorylates• Glycosylates• Ubiquitinates• Interacts with
Hindrance
• Doing pair-wise queries for all N proteins in an organism requires N*N queries
• E. coli has >4000 genes
(16,000,000 queries)
• Yahoo! allows 5k / day / computer
Better Method
• Only specify the first symbol
• Iterate through the results and only
take results whose word following the
joining symbol corresponds to a valid
symbol
Another Hindrance
• The symbol “thE”(and others like it)
• Searches need to be case insensitive to account for “p53” and “P53”
• Recognizes the word “the” as the protein “thE”
Solution
• Use a list of stop words
• Very common, non-interesting
words
• If the name appears in that list of stop
words, just forget about that protein
all together
http://www.dcs.gla.ac.uk/idom/ir_resources/linguistic_utils/stop_words
Methods (cont.)
• After we have this data in a database...
• Create a web interface to the data so others can search for protein interactions (Shwe)
Data (cont.)
KEGG - Yeast Cell Cycle
http://www.genome.jp/dbget-bin/get_pathway?org_name=sce&mapno=04110
Our Data
Data (cont.)
KEGG - Yeast 26S Proteasome
Our Data
http://www.genome.jp/dbget-bin/show_pathway?sce03050+YER012W
Final Remarks
• Only works well detecting signal pathways and protein complexes
• Not metabolic pathways
• It is possible to get high quality, interesting data without much noise or complex text analysis algorithms
References
• Kyoto Encyclopedia of Genes and Genomes http://www.genome.jp/kegg/
• Cytoscape Network Visualization http://www.cytoscape.org/
• Yahoo! Developer Network http://developer.yahoo.com/