The document summarizes two demonstrations of the SADI and SHARE frameworks. The first demonstration shows how a SPARQL query is used to retrieve pathway information for a protein from multiple distributed data sources without using a database. The second demonstration shows clinical data for transplant patients being rejected being retrieved through analyzing definitions in an ontology and executing appropriate services.

1. SADI, SHARE and the Scientific MethodThe Quest for the Holy Grail

2. The Problem

3. The Problem

4. The Holy Grail:(this slide created circa 2002)Align the promoters of all serine threoninekinases involved exclusively in the regulation of cell sorting during wound healing in blood vessels.Retrieve and align 2000nt 5' from every serine/threonine kinase in Mus musculus expressed exclusively in the tunica [I | M |A] whose expression increases 5X or more within 5 hours of wounding but is not activated during the normal development of blood vessels, and is <40% homologous in the active site to kinases known to be involved in cell-cycle regulation in any other species.

5. Two novel technologies developed in our labare getting us very close to the Holy Grail!

6. Holy Grail Demo #1

7. Imagine there is a “virtual database” containing all of the data from all of the databases,together with the output ofevery conceivable analysis

8. How do we query that database?

9. A Brief Digression…

10. “Database”

14. ?

15. Boxes became ovals…Straight lines became curvy lines…

16. Boxes became ovals…Straight lines became curvy lines……and you want us to give you a grant for THAT??

17. Relational Database“Graph”

18. Gene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates IDisRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

19. “Foreign keys” are used to link tables in a databaseGene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates IDisRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

20. Gene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates IDLinks in Graphs consist of statements called“TRIPLES” isRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

21. Both Data Sources are on the Same MachineGene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates IDisRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

22. Gene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates IDGraph Data Sources (may be) on Independent Machines on the WebisRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

23. “Meaning” of the connection between data-points is understood only by the database administratorProtein regulates GeneGene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates IDisRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

24. Gene Table-----------------------Gene IDTissue IDType IDProtein Table-----------------------Protein IndexProtein NameRegulates ID“Meaning” of the connection in a Graph is explicitly labeled(and machine-readable!)isRepressorOfhttp://ncbi.nlm/NR/NR_14487http://pdb.org/114487

25. Connect all of the graphs in the world to one anotherAnd what do you get?

26. Mark Butler (2003) Is the semantic web hype? Hewlett Packard laboratories presentation at MMU, 2003-03-12

27. The lavender portion represents biology – currently ~40,000,000,000 Triples(we and our collaborators will be doubling that number in the next 12 months)

28. How do you find information on this“Semantic Web”??

29. SPARQLThe query language used to discover and extract information represented in Graphs

30. SPARQLUnfortunately, YOU have to know which Web resources contain which Triples(HARD!)Even if you do know this, SPARQL has significant limitations when attempting to query over disparate Graphs(SLOW AND CUMBERSOME)

31. SPARQLIf the data doesn’t existin any Graph at all…

33. Basically…A novel way of making Triples available on the Semantic Web, using a technology called Web Services“Services” for short

34. Basically…We invented SADI to overcome some/all of these problems…but I wont bore you with the technical details…

35. Detour EndsPlease resume speed

36. Holy Grail Demo #1Imagine there is a “virtual database” containing all of the data from all of the databases,together with the output ofevery conceivable analysisHow do we query that database?

37. SHARESemantic Health And Research EnvironmentSPARQL enhanced by SADI

38. A Novel SPARQL Query EngineOvercomes some of the limitations of traditional SPARQL query-handlers

39. A Novel SPARQL Query EngineOvercomes some of the limitations of traditional SPARQL query-handlers…and more…

40. A Novel SPARQL Query EngineOvercomes some of the limitations of traditional SPARQL query-handlers…and more…MUCH more!!

41. What pathways does UniProt protein P47989 belong to?PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>PREFIX ont: <http://ontology.dumontierlab.com/>PREFIX uniprot: <http://lsrn.org/UniProt:>SELECT ?gene ?pathway WHERE { uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway . }

42. What pathways does UniProt protein P47989 belong to?PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>PREFIX ont: <http://ontology.dumontierlab.com/>PREFIX uniprot: <http://lsrn.org/UniProt:>SELECT ?gene ?pathway WHERE { uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway . }

43. What pathways does UniProt protein P47989 belong to?PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>PREFIX ont: <http://ontology.dumontierlab.com/>PREFIX uniprot: <http://lsrn.org/UniProt:>SELECT ?gene ?pathway WHERE { uniprot:P47989pred:isEncodedBy ?gene . ?geneont:isParticipantIn ?pathway . }Note that there is no “From” clause… I have neglected to tell the system where to look for the answer, I am simply asking my question

44. Now stick that query into SHARE

47. Recapwhat we just sawA standard SPARQL query was entered into SHARE, a SADI-aware query engine

48. Recapwhat we just sawThe query was interpreted to extract the individual data/relationships being requested (and any component/sub-properties, as we shall see later!)

49. Recapwhat we just sawThe “triple-patterns” required to answer the query are passed to SADI for Web Service discovery

50. Recapwhat we just sawServices capable of generating those triple-patterns are automatically executed, the triples are stored, and the query is resolved.

51. Recapwhat we just sawWe posed, and answered a ~complex database query WITHOUT A DATABASE(in fact, the data didn’t even have to exist...)

52. Holy Grail Demo #1Align the promoters of all serine threonine kinases involved exclusively in the regulation of cell sorting during wound healing in blood vessels.Retrieve and align 2000nt 5' from every serine/threonine kinase in Mus musculus expressed exclusively in the tunica [I | M |A] whose expression increases 5X or more within 5 hours of wounding but is not activated during the normal development of blood vessels, and is <40% homologous in the active site to kinases known to be involved in cell-cycle regulation in any other species.

53. Holy Grail Demo #2

54. Show me the latest Blood Urea Nitrogen and Creatinine levelsof patients who appear to be rejecting their transplantsPREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#> PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE { ?patientrdf:typepatient:LikelyRejecter . ?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat . }

55. Likely Rejecter:A patient who has creatinine levelsthat are increasing over time- - Wilkinson MD

56. Likely Rejecter:…but there is no “likely rejecter” column or table in our database… only blood chemistry measurementsat various time-points

57. ?

58. The definition of a LikelyRejecter is encoded in a machine-readable document written in the OWL language (“Ontology”)“the regression line over creatinine measurements should have an increasing slope”

59. The machine continues to burrow down through the definition and discovers that regression lines have things like slopes and intercepts, etc…

60. Then… Two magical events occur…

61. The machine figures outby itselfthe need to do a Linear Regression analysisin order to answer your question

62. The machine figures outby itselfhow and wherethat analysiscan be doneand does it automatically!

63. http://www.impactlab.net/2009/03/22/improve-your-brain-power/

64. The SHARE system utilizes SADI to discover analytical services on the Web that do linear regression analysis

65. VOILA!

66. How do we do that?!?We let the data describe itself!This is a different frommost of the bioinformatics world,where the person giving you the data also tells you how to interpret it

67. Data exhibits “late binding”

69. Late binding:“purpose and meaning”of the data isnot determined untilthe moment it is required

70. Benefitof late bindingData is amenable toconstant re-interpretation

71. Example?Blood Creatinine measurementswere not dictated to be (only)Blood Creatinine measurements!

72. Example?The data had the ‘qualities/properties’ thatallowed the machine to inferthat they were Blood Creatinine measurements

73. Example?But the data also had the ‘qualities/properties’ that allowed them to be interpreted as X/Y coordinate data by another Service

74. http://www.flickr.com/people/faernworks/

75. Holy Grail Demo #2Align the promoters of all serine threonine kinases involved exclusively in the regulation of cell sorting during wound healing in blood vessels.Retrieve and align 2000nt 5' from every serine/threonine kinase in Mus musculus expressed exclusively in the tunica [I | M |A] whose expression increases 5X or more within 5 hours of wounding but is not activated during the normal development of blood vessels, and is <40% homologous in the active site to kinases known to be involved in cell-cycle regulation in any other species.

76. The Holy Grail may not yet be in-handbut we can at least see it from here!So… now what?

77. Mark’s ManifestoWhat is my next “Holy Grail”?

78. ScienceSupport for the in silicoScientific Method

80. The Scientific MethodDiscourse: What do you believe? What do I believe?Disagreement: You’re wrong! And I’m gonna prove it!Clarity: This is the experiment I am going to doReproducibility: This is how I did it (“provenance”)Clarity: This is my new hypothesis

81. The Scientific MethodDiscourse: What do you believe? What do I believe?Disagreement: You’re wrong! And I’m gonna prove it!Clarity: This is the experiment I am going to doReproducibility: This is how I did it (“provenance”)Clarity: This is my new hypothesisWorkflows (e.g. myExperiment)

82. Another Brief Digression…

83. “Facebook” for Scientistshttp://myexperiment.org

84. An exciting evolution in the way Researchers express and share their in silico “Materials and Methods”Through things called ‘Workflows’

86. Workflows are explicit representationsof the method by which an analysis was doneand which resources are used to do it

87. Workflows can be very simple… “Blast this sequence”

88. Or not...This workflow takes in a CEL file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the MADAT software package (MicroArray Data Analysis Tool) Also returned by this workflow are a list of the top differentially expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data.

89. Why bother?

90. TavernaA workbench for designing and executingScientific Workflows

92. Load-up your data and press “play”!…Then go home for the weekend! You are just one click away from your M.Sc.!!

93. By the by…The SHARE application automatically creates a Workflow and then automatically runs it.This is where the data comes from to answer the queries… Workflows are a Good Thing™

94. Detour EndsPlease resume speed

95. WORKFLOWS

97. At the moment the Semantic Web in Healthcare and Life Sciencesaddresses these issues by attempting to create “consensus”

98. Large, centralized ontologies (e.g. the Gene Ontology)that claim to represent community agreement about “biological reality”

99. …is that Science?

104. To restore the “traditions of Science” to in silico scienceThe Semantic Web needs to encourage/facilitate personal opinion and debate

105. What has this got to do with SADI and SHARE?

106. PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#> PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE { ?patientrdf:typepatient:LikelyRejecter . ?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat . }

107. Likely Rejecter

108. I created a small ontology describing my definition ofa Likely Rejecter

109. … it was MY ontology!

110. I can re-use it

111. I can modify it as I change myworld-view

112. I can publish it for others to use

113. Others can modify it and/or compare it to THEIR world-view

114. Sharing my ontology gives opportunities for “micro-attribution”“Credit” to me is automatic when someone uses my ontology in their ontology/query

115. Using SADI and SHAREmypersonal world-view isexplicitlyexpressedand can bedynamically evaluated againstglobal data and knowledge

116. http://www.dailymail.co.uk/femail/article-488234/Friends-dignity-self-respect---weight-wasnt-I-lost-slimming-club.html

117. …but there’s more…

118. “Likely Rejecter”

119. I made that up! It came out of my head!

120. What’s another word for a world-view that you make-up?Hypothesis

121. The “Likely Rejecter” OWL Classis an explicitly-expressed hypothesis;Members of that class may or may not exist!

124. Ontologically-expressed Hypotheses drive the discovery, assembly, and analysis of data capable of evaluating their validityHypothesisIschemiaSADI+ SHAREHypertensionBlood PressureAnalytical AlgorithmDatabase 1Database 2

125. Join us!SADI and CardioSHARE are Open-Source projectsCome join us – we’re having a lot of fun!!http://sadiframework.org

126. CreditsBenjamin VanderValk(SHARE & SADI)Luke McCarthy (SADI, SHARE, Taverna, CardioSHARE)SoroushSamadian(CardioSHARE)David Withers(Taverna)Edward Kawas(SADI Service auto-generator)

127. U of New BrunswickDr. Chris BakerAlexandreRiazanovCarleton UniversityDr. Michel Dumontier Marc-Alexandre Nolin Leonid Chepelev Steve EtlingerNichaellaKieth Jose Cruz

128. Microsoft Research

129. CreditsBenjamin VanderValk (SADI & CardioSHARE)Luke McCarthy (SADI & CardioSHARE)SoroushSamadian (CardioSHARE)IO Informatics (Knowledge Explorer API)Microsoft Research FinThis presentation available on SlideShare: keywords ‘wilkinson’ ‘iCAPTURE’ ‘HLI’