Talk given at the Genome Informatics conference 2012 at Robinson College, Cambridge University.

1. GeneGames.org
The Gene Wiki: Crowdsourcing
human gene annotation
Andrew Su, Ph.D.
@andrewsu
asu@scripps.edu
http://sulab.org OK
Genome Informatics OK
September 6, 2012

2. 2
The Gene Wiki crib sheet
http://www.slideshare.net/andrewsu
• Bulk creation of ~10k Wikipedia articles
(http://dx.doi.org/10.1371/journal.pbio.0060175)
• Monthly stats: > 4 million views, > 1000 edits
(http://dx.doi.org/10.1093/nar/gkr925)
• Text mining reveals novel Gene Ontology and Disease
Ontology annotations (http://dx.doi.org/doi:10.1186/1471-2164-
12-603)
• Mash-up with SNPedia for crowdsourced gene-
disease database (http://www.jbiomedsem.com/content/3/S1/S6)
• Merging Wikipedia with the Semantic Web
(http://dx.doi.org/10.1093/database/bar060)

3. 3
Seven million human hours
http://www.flickr.com/photos/archana3k1/4124330493/

4. 4
Twenty million human hours
http://www.flickr.com/photos/ableman/2171326385/

5. 5
-
150 billion human hours
per year
http://www.flickr.com/photos/rvp-cw/6243289302/

6. 6
Using games to fold proteins
Fold.it players have successfully:
• Outperformed state of the art protein
folding algorithms (Cooper, Nature, 2010)
• Solved a previously-intractable crystal
structure (Khatib, Nat Struct Mol Biol, 2011)
• Designed an improved protein folding
algorithm (Khatib, PNAS, 2011)
• Improved enzyme activity of de novo
designed enzyme (Eiben, Nat Biotechnol, 2011)
http://fold.it

7. 7
Using games to fold RNAs
http://eterna.cmu.edu/

8. 8
Using games to align sequences
http://phylo.cs.mcgill.ca

9. 9
Using games to annotate genes?
http://genegames.org

10. 10
No good gene-disease annotation database
Query: Apolipoprotein E
Alzheimer's disease (AD)
Lipoprotein glomerulopathy
Sea-blue histiocyte disease

11. 11
No good gene-disease annotation database
Query: Apolipoprotein E
Alzheimer's disease (AD)
Lipoprotein glomerulopathy
Sea-blue histiocyte disease
Hyperlipoproteinemia, type III
Macular degeneration, age-related
Myocardial infarction susceptibility

12. 12
No good gene-disease annotation database
Query: Apolipoprotein E
? Alzheimer's disease (AD)
? Lipoprotein glomerulopathy
? Sea-blue histiocyte disease
Hyperlipoproteinemia, type III
? Macular degeneration, age-related
? Myocardial infarction susceptibility
HIV
Psoriasis
Vascular Diseases

13. 13
No good gene-disease annotation database
Query: Apolipoprotein E
Alzheimer's disease (AD) Memory
Coronary Artery Disease
Neuropsychological Tests Hypertension
Cognition Disorders Mental Status Schedule
Psychiatric Status Rating
Dementia Scales
Cognition Hyperlipidemias
Atrophy
Disease Progression Dementia, Vascular
Cardiovascular Diseases Parkinson Disease
Brain Injuries
Coronary Disease Myocardial Infarction
Diabetes Mellitus, Type 2 …
Memory Disorders 477 diseases!

14. 14
Play Dizeez to annotate gene-disease links
6. Play to win!
5. Hurry!
4. Then on to the
next question…
3. If it‟s „right‟, you get points
1. Read the clue (gene)
2. Click the related disease
(only one is “right”)

15. 15
Dizeez players seem pretty smart…
In total (since Dec 2011):
• 207 unique gamers
• 1045 games played
• 8525 guesses
# Occurrences Gene Disease Pubmed OMIM PharmGKB Gene Wiki
7 GAST gastrinoma
7 RBP3 retinoblastoma
7 SSX1 synovial sarcoma
6 TG Graves' disease
6 CRYGC Cataract
6 SOX8 mental retardation
6 WRN Werner syndrome
6 ABL1 leukemia
6 MLL3 leukemia
6 SNAI2 breast carcinoma

16. 16
Dizeez players seem pretty smart…
In total (since Dec 2011):
• 207 unique gamers
• 1045 games played
• 8525 guesses
# Occurrences Gene Disease Pubmed OMIM PharmGKB Gene Wiki
5 MECOM sarcoma
4 ATF7 cancer
3 ABCB5 acute myeloid leukemia
3 SART1 glioblastoma
3 NCK1 leukemia
3 NEK1 cancer

17. 17
Using games to predict phenotype from genotype?
The Cure
http://genegames.org

18. 18
Classification problems in genome biology
Classify new
cancer normal samples
find patterns
cancer
100,000s features
normal
SVM
Neural
networks
Naïve
Bayes
KNN
…
100s samples

19. 19
Random forests
Sample subset
of cases and Train decision
cancer normal features tree
100,000s features
100s samples

20. 20
Random forests
cancer normal
100,000s features
100s samples

21. 21
Random forests
Classify new
cancer normal samples
cancer
100,000s features
normal
How to interject
biological
100s samples knowledge?

22. 22
Network-guided forests
Dutkowski & Ideker (2011). PLoS Computational Biology

23. 23
Network-guided forests
Sample
features by PPI Train decision
cancer normal network tree
100,000s features
100s samples

24. 24
Human-guided forests
Sample
features by Train decision
cancer normal human tree
intelligence
100,000s features
100s samples

25. 25
The Cure: Genomic predictors for disease

26. 26
The Cure: Genomic predictors for disease

27. 27
The Cure: Genomic predictors for disease

28. 28
The Cure: Genomic predictors for disease

29. 29
The Cure: Genomic predictors for disease

30. 30
The Cure: Genomic predictors for disease

31. 31
Human-guided forests
Classify new
samples
cancer
normal

32. 32
“Critical Assessment”-style challenge
Will this work? Check our blog after October 15.

33. 33
Collaborators Group members
Doug Howe, ZFIN Ben Good Max Nanis
John Hogenesch, U Penn
Jon Huss, GNF
Salvatore Loguercio Chunlei Wu
Luca de Alfaro, UCSC Ian Macleod
Angel Pizzaro, U Penn
Faramarz Valafar, SDSU
Pierre Lindenbaum,
Fondation Jean Dausset
Michael Martone, Rush
Konrad Koehler, Karo Bio
Warren Kibbe, Simon Lim, Northwestern
Many Wikipedia editors
WP:MCB Project
Contact
http://sulab.org
Recruiting graduate students
asu@scripps.edu
in quantitative biology! See @andrewsu
http://education.scripps.edu/ +Andrew Su
Funding and Support
@genegame
(BioGPS: GM83924, Gene Wiki: GM089820)