Crowdsourcing Biology: The Gene Wiki, BioGPS and GeneGames.org
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Given at DBMI seminar series at UCSD. http://dbmi.ucsd.edu/display/DBMI/Seminars
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Crowdsourcing Biology: The Gene Wiki, BioGPS and GeneGames.org
- 1. Crowdsourcing Biology: The Gene Wiki, BioGPS and GeneGames.org Andrew Su, Ph.D. @andrewsu asu@scripps.edu http://sulab.org April 5, 2013 UCSD DBMI Seminar
- 2. Few genes are well annotated… 2 Data: NCBI, February 2013 41% 65% CTNNB1 VEGFA SIRT1 FGFR2 TGFB1 TP53 MEF2C BMP4 LEF1 WNT5A TNF 20,473 protein- coding genes Genes, sorted by decreasing counts GOAnnotation Counts
- 3. 0 200,000 400,000 600,000 800,000 1,000,000 1979 1984 1989 1994 1999 2004 2009 Number of PubMed-indexed articles … because the literature is sparsely curated? 3
- 4. … because the literature is sparsely curated? 4 0 10 20 1979 1984 1989 1994 1999 2004 2009 Average capacity of human scientistNumber of articles read by typical scientist
- 5. 5 311,696 articles (1.5% of PubMed) have been cited by GO annotations
- 6. 6 0 Sooner or later, the research community will need to be involved in the annotation effort to scale up to the rate of data generation.
- 7. The Long Tail is a prolific source of content 7 Short Head Long Tail Content produced Contributors (sorted) News : Video: Product reviews: Food reviews: Talent judging: Newspapers TV/Hollywood Consumer reports Food critics Olympics Blogs YouTube Amazon reviews Yelp American Idol
- 8. Wikipedia is reasonably accurate 8
- 9. Wikipedia has breadth and depth 9 http://en.wikipedia.org/wiki/Wikipedia:Size_comparisons, July 2008 Articles Words (millions) Wikipedia Britannica Online
- 10. 10 We can harness the Long Tail of scientists to directly participate in the gene annotation process.
- 11. From crowdsourcing to structured data 11 The Gene Wiki Biological Games
- 12. Filtering, extracting, and summarizing PubMed Documents Concepts Review article
- 13. Filtering, extracting, and summarizing PubMed Documents Concepts
- 14. Wiki success depends on a positive feedback 14 Gene wiki page utility Number of users Number of contributors 1001 2002
- 15. 10,000 gene “stubs” within Wikipedia 15 Protein structure Symbols and identifiers Tissue expression pattern Gene Ontology annotations Links to structured databases Gene summary Protein interactions Linked references Huss, PLoS Biol, 2008 Utility Users Contributors
- 16. Gene Wiki has a critical mass of readers 16 Total: 4.0 million views / month Huss, PLoS Biol, 2008; Good, NAR, 2011 Utility Users Contributors
- 17. Gene Wiki has a critical mass of editors 17 Increase of ~10,000 words / month from >1,000 edits Currently 1.42 million words Approximately equal to 230 full-length articles Good, NAR, 2011 Utility Users Contributors Editorcount Editors Edits Editcount
- 18. A review article for every gene is powerful 18 References to the literature Hyperlinks to related concepts Reelin: 98 editors, 703 edits since July 2002 Heparin: 358 editors, 654 edits since June 2003 AMPK: 109 editors, 203 edits since March 2004 RNAi: 394 editors, 994 edits since October 2002
- 19. Making the Gene Wiki more computable 19 Structured annotationsFree text
- 20. Filling the gaps in gene annotation 20 Wikilink GO exact match Gene Wiki mapping NCBI Entrez Gene: 334 Candidate assertion GO:0006897 6319 novel GO annotations 2147 novel DO annotations
- 21. Gene Wiki content improves enrichment analysis 21 GO term Gene list Concept recognition PubMed abstracts Enrichment analysis GO:0007411 axon guidance (GO:0007411) 264 genes Linked genes through PubMed P = 1.55 E-20 811 articles Yes No Yes 13 2 No 251 12033
- 22. Gene Wiki content improves enrichment analysis 22 GO term Gene list Concept recognition PubMed abstracts Gene Wiki + Enrichment analysis GO:0006936 GO:0006936 muscle contraction (GO:0006936) 87 genes Linked genes through PubMed Linked genes through PubMed + Gene Wiki P = 1.0 P = 1.22 E-09 251 articles 87 articles
- 23. Gene Wiki content improves enrichment analysis 23 p-value (PubMed only) p-value (PubMed + GW) Muscle contraction More significant PubMed + GW More significant PubMed only
- 24. Making the Gene Wiki more computable 24 Structured annotationsFree text Analyses
- 25. Making the Gene Wiki more computable 25 Structured annotationsFree text Databases
- 26. Making the Gene Wiki more computable 26 Databases Linked Data
- 27. The Long Tail of scientists is a valuable source of information on gene function 27
- 28. From crowdsourcing to structured data 28 The Gene Wiki Biological Games
- 29. Gene databases are numerous and overlapping 29 … and hundreds more …
- 30. Why is there so much redundancy? 30 Users Requests Resources Time Community development BioGPS emphasizes community extensibility
- 31. Why do developers define the gene report view? 31 BioGPS emphasizes user customizability
- 32. http://biogps.org Community extensibility and user customizability 32
- 33. Utility: A simple and universal plugin interface 33 KEGG http://www.genome.jp/dbget-bin/www_bget?hsa:{{EntrezGene}} STRING http://string-db.org/newstring_cgi?...&identifier={{EnsemblGene}} Pubmed http://www.ncbi.nlm.nih.gov/sites/entrez?...&Term={{Symbol}} URL template Gene entity Rendered URL
- 34. Utility UsersContributors Utility: A simple and universal plugin interface 34
- 35. Utility UsersContributors Utility: A simple and universal plugin interface 35
- 36. Utility UsersContributors Utility: A simple and universal plugin interface 36
- 37. Utility UsersContributors Utility: A simple and universal plugin interface 37
- 38. Utility UsersContributors Utility: A simple and universal plugin interface 38
- 39. Utility: A simple and universal plugin interface 39 Utility UsersContributors Total of > 540 gene-centric online databases registered as BioGPS plugins
- 40. Users: BioGPS has critical mass 40 • > 6400 registered users • 14,000 unique visitors per month • 155,000 page views per month 1. Harvard 2. NIH 3. UCSD 4. Scripps 5. MIT 6. Cambridge 7. U Penn 8. Stanford 9. Wash U 10. UNC Top 10 organizations Daily pageviewsUtility UsersContributors
- 41. Contributors: Explicit and implicit knowledge 41 540 plugins registered (>300 publicly shared) by over 120 users spanning 280+ domains Utility UsersContributors
- 42. All resources should provide RDF… 42
- 43. Mining structured content from HTML 43
- 44. Defining a data extraction template 44 … TP53 TNF APOE IL6 VEGF …EGFR TGFB1
- 45. The BioGPS Semantic Annotator 45 http://54.244.135.254:8080
- 46. The Long Tail of bioinformaticians can collaboratively build a gene portal. 46
- 47. From crowdsourcing to structured data 47 The Gene Wiki Biological Games
- 49. 49 Twenty million human hours http://www.flickr.com/photos/ableman/2171326385/
- 50. - 50 150 billion human hours http://www.flickr.com/photos/rvp-cw/6243289302/ per year
- 51. Using games to fold proteins 51 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)
- 52. Using games to fold RNAs 52 http://eterna.cmu.edu/
- 53. Using games to align sequences 53 http://phylo.cs.mcgill.ca
- 54. Using games to diagnose malaria infection 54 http://biogames.ee.ucla.edu/
- 55. Using games to map neurons 55 http://eyewire.org
- 56. Using games to annotate genes? 56 http://genegames.org
- 57. No good gene-disease annotation database 57 Alzheimer's disease (AD) Lipoprotein glomerulopathy Sea-blue histiocyte disease Query: Apolipoprotein E
- 58. No good gene-disease annotation database 58 Alzheimer's disease (AD) Lipoprotein glomerulopathy Sea-blue histiocyte disease Hyperlipoproteinemia, type III Macular degeneration, age-related Myocardial infarction susceptibility Query: Apolipoprotein E
- 59. No good gene-disease annotation database 59 Alzheimer's disease (AD) Lipoprotein glomerulopathy Sea-blue histiocyte disease Hyperlipoproteinemia, type III Macular degeneration, age-related Myocardial infarction susceptibility HIV Psoriasis Vascular Diseases Query: Apolipoprotein E ? ? ? ? ?
- 60. No good gene-disease annotation database 60 Alzheimer's disease (AD) Neuropsychological Tests Cognition Disorders Dementia Cognition Disease Progression Cardiovascular Diseases Coronary Disease Diabetes Mellitus, Type 2 Memory Disorders Query: Apolipoprotein E Memory Coronary Artery Disease Hypertension Mental Status Schedule Psychiatric Status Rating Scales Hyperlipidemias Atrophy Dementia, Vascular Parkinson Disease Brain Injuries Myocardial Infarction … 477 diseases!
- 61. Play Dizeez to annotate gene-disease links 61 3. If it‟s „right‟, you get points 4. Then on to the next question… 2. Click the related disease (only one is “right”) 5. Hurry! 1. Read the clue (gene) 6. Play to win!
- 62. Dizeez players seem pretty smart… 62 In total (since Dec 2011): • 230 unique gamers • 1045 games played • 8525 guesses # Occurrences Gene Disease 11 NBPF3 neuroblastoma 11 SOX8 mental retardation 9 ABL1 leukemia 9 SSX1 synovial sarcoma 8 APC colorectal cancer 8 FES sarcoma 8 RBP3 retinoblastoma 8 GAST gastrinoma 8 DCC colorectal cancer 8 MAP3K5 cancer Gene Wiki OMIM PharmGKB PubMed
- 63. Using games to predict phenotype from genotype? 63 http://genegames.org
- 64. Classification problems in genome biology 64 cancer normal find patterns Classify new samples cancer normal SVM Neural networks Naïve Bayes KNN … 100s samples 100,000sfeatures
- 65. Random forests 65 Sample subset of cases and features Train decision treecancer normal 100s samples 100,000sfeatures
- 66. Random forests 66 cancer normal 100s samples 100,000sfeatures
- 67. Random forests 67 Classify new samples cancer normal cancer normal 100s samples 100,000sfeatures How to interject biological knowledge?
- 68. Network-guided forests 68 Dutkowski & Ideker (2011). PLoS Computational Biology
- 69. Network-guided forests 69 Sample features by PPI network Train decision treecancer normal 100s samples 100,000sfeatures
- 70. Human-guided forests 70 Sample features by human intelligence Train decision treecancer normal 100s samples 100,000sfeatures
- 71. 71
- 72. The Cure: Genomic predictors for disease 72
- 73. The Cure: Genomic predictors for disease 73
- 74. The Cure: Genomic predictors for disease 74
- 75. The Cure: Genomic predictors for disease 75
- 76. The Cure: Genomic predictors for disease 76
- 77. The Cure: Genomic predictors for disease 77
- 80. Results • 214 registered players – 50% declared knowledge of cancer biology – 40% self-identified as having Ph.D. • Prediction results – 70% correct on survival concordance index – Best scoring model was 76% – Player registrations still increasing! 80
- 81. The Long Tail of gamers can collaboratively build an accurate disease classifier. 81
- 82. 82 Doug Howe, ZFIN John Hogenesch, U Penn Jon Huss, GNF Luca de Alfaro, UCSC 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 Collaborators Katie Fisch Ben Good Salvatore Loguercio Max Nanis Chunlei Wu Group members Funding and Support (BioGPS: GM83924, Gene Wiki: GM089820) Contact http://sulab.org asu@scripps.edu @andrewsu +Andrew Su Adriel Carolino Erik Clarke Jon Huss Marc Leglise Maximilian Ludvigsson Ian MacLeod Camilo Orozco Key group alumni
- 83. Doctoral Program in Chemical and Biological Sciences CALIFORNIA Office of Graduate Studies 10550 N. Torrey Pines Road La Jolla, CA 92037 Email: gradprgrm@scripps.edu Phone: 858.784.8469 http://education.scripps.edu
Notes de l'éditeur
- We are very early in our efforts to comprehensively annotate human gene functionWhy important? Genome-scale surveys aren’t biased toward well studied genes, huge opportunity for biomedical discoveryNo IEA
- If you believe that greater than 1.5% of articles have relevance to gene function, then it says there is a bottleneck in in our curation effortsNumbers updated 7/15/2011
- For each resourceBriefly describe the unstructured resourceDescribe the structuring approach
- Relying on the entire community of scientists to digest the biomedical literature: identification filtering extraction summarization
- Relying on the entire community of scientists to digest the biomedical literature: identification filtering extraction summarization
- Structured annotations enable pathway analysis, statistical analyses, cross-species comparisons
- Tried on 773 GO categories, significant in 356 cases (46%)
- We extended this analysis to all 773 GO terms used in human gene annotations and found a consistent improvement in the enrichment scores
- Structured annotations enable pathway analysis, statistical analyses, cross-species comparisons
- Also want to convince you that the Long Tail of bioinformatics developers is valuable too, but first have to convince you that there is a bottleneck in tool development.
- For each resourceBriefly describe the unstructured resourceDescribe the structuring approach
- Developer resources do not scale with usagePractical effects:Core developers’ time is always the rate-limiting step Addition of new features and data always feels slowEventually, new databases are created to fill the gap80% duplication for 20% innovation
- MODs and portals
- Genetics resources
- Literature resources
- Protein resources
- Pathway and expression databases
- Pathway and expression databases
- Also want to convince you that the Long Tail of bioinformatics developers is valuable too, but first have to convince you that there is a bottleneck in tool development.
- For each resourceBriefly describe the unstructured resourceDescribe the structuring approach
- Empire state building
- Question: how to interject biological knowledge in the feature selection process?
- Kellogg School slide.pptx