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![Interaction Context
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[WT]
Interaction
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[mutant]
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Description DescriptionProtocol Protocol
“Historical
observation”
“Base
state” “reduced
virulence”
“soft rot”
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descriptionCitation
PubMed
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“PMID:1234”
“gene
deletion”
Environmental
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Environmental
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Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)](https://image.slidesharecdn.com/ibcfairprototypeimplementationslideshow-170118085918/75/IBC-FAIR-Data-Prototype-Implementation-slideshow-108-2048.jpg)
![Interaction Context
Interaction
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[WT]
Interaction
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[mutant]
Host
Context
Host
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Pathogen
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Pathogen
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Description DescriptionProtocol Protocol
“Historical
observation”
“Base
state” “reduced
virulence”
“soft rot”
Protocol
descriptionCitation
PubMed
ID
“PMID:1234”
“gene
deletion”
Environmental
Context
Environmental
Context
Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)](https://image.slidesharecdn.com/ibcfairprototypeimplementationslideshow-170118085918/75/IBC-FAIR-Data-Prototype-Implementation-slideshow-109-2048.jpg)
![Interaction Context
Interaction
Context
[WT]
Interaction
Context
[mutant]
Host
Context
Host
Context
Pathogen
Context
Pathogen
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Description DescriptionProtocol Protocol
“Historical
observation”
“Base
state” “reduced
virulence”
“soft rot”
Protocol
descriptionCitation
PubMed
ID
“PMID:1234”
“gene
deletion”
Environmental
Context
Environmental
Context
Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)](https://crownmelresort.com/image.slidesharecdn.com/ibcfairprototypeimplementationslideshow-170118085918/75/IBC-FAIR-Data-Prototype-Implementation-slideshow-108-2048.jpg)
![Interaction Context
Interaction
Context
[WT]
Interaction
Context
[mutant]
Host
Context
Host
Context
Pathogen
Context
Pathogen
Context
Description DescriptionProtocol Protocol
“Historical
observation”
“Base
state” “reduced
virulence”
“soft rot”
Protocol
descriptionCitation
PubMed
ID
“PMID:1234”
“gene
deletion”
Environmental
Context
Environmental
Context
Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)](https://crownmelresort.com/image.slidesharecdn.com/ibcfairprototypeimplementationslideshow-170118085918/75/IBC-FAIR-Data-Prototype-Implementation-slideshow-109-2048.jpg)
Uploaded byMark Wilkinson
IBC FAIR Data Prototype Implementation slideshow
The document discusses a novel, API-free approach to improving data interoperability within legacy and prospective datasets, emphasizing the importance of FAIR principles: Findable, Accessible, Interoperable, and Reusable. It highlights the problems of data reproducibility and completeness in ecological and evolutionary research and outlines a concept for creating 'fair accessors' to facilitate publishing and accessing metadata about various scholarly entities without relying on APIs. The discussion involves collaborative efforts and proposed solutions aimed at enhancing data quality for better research transparency and reproducibility.
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IBC FAIR Data Prototype Implementation slideshow
- 1. Mark D. Wilkinson CBGP-UPM/INIA,Madrid markw@illuminae.com A novel, API-free approach to interoperability leads to FAIRness for legacy and prospective data. IBC Scientific Days January 17-18, 2017
- 2. The Problem ...one recentsurvey of 18 microarray studies found that only two were fully reproducible using the archived data. Another study of 19 papers in population genetics found that 30% of analyses could not be reproduced from the archived data and that 35% of datasets were incorrectly or insufficiently described. “ ” Dominique G. Roche , Loeske E. B. Kruuk, Robert Lanfear, Sandra A. Binning (2015) http://dx.doi.org/10.1371/journal.pbio.1002295
- 3. The Problem We surveyed100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. “ ” Dominique G. Roche , Loeske E. B. Kruuk, Robert Lanfear, Sandra A. Binning (2015) http://dx.doi.org/10.1371/journal.pbio.1002295
- 4. The Problem Is thatdata, therefore... Useless?
- 5.
- 6. The Problem It’s Reuseless! h.t.to Barend Mons
- 7. FAIR Findable → Globally unique,resolvable, and persistent identifiers → Machine-actionable contextual information supporting discovery Accessible → Clearly-defined access protocol → Clearly-defined rules for authorization/authentication Interoperable → Use shared vocabularies and/or ontologies → Syntactically and semantically machine-accessible format Reusable → Be compliant with the F, A, and I Principles → Contextual information, allowing proper interpretation → Rich provenance information facilitating accurate citation The Four Principles
- 8. “Skunkworks” Task: Build aprototype
- 9. Skunkworks Participants Mark Wilkinson MichelDumontier Barend Mons Tim Clark Jun Zhao Paolo Ciccarese Paul Groth Erik van Mulligen Luiz Olavo Bonino da Silva Santos Matthew Gamble Carole Goble Joël Kuiper Morris Swertz Erik Schultes Erik Schultes Mercè Crosas Adrian Garcia Philip Durbin Jeffrey Grethe Katy Wolstencroft Sudeshna Das M. Emily Merrill
- 10. The Hourglass Concept Wewant a large ecosystem of apps that use FAIR Data
- 11. The Hourglass Concept Wewant to support a wide range of source providers
- 12. The Hourglass Concept TheFAIR solution between them must be THIN!
- 13. Skunkworks participants had tonsof experience v.v. metadata around scholarly publication
- 14. Skunkworks participants had tonsof experience v.v. metadata around scholarly publication RDA, Force11, Dataverse, Research Objects, NanoPubs, Semantic Science, SADI, AlzForum, SWAN, LSID, … … … ...
- 15. There was verylittle disagreement about F, about A, or about R
- 16. The “I” isthe big problem
- 17. Interoperability is Hard!! The “I”is the big problem
- 18. Keeping the historybrief A series of teleconferences led to the concept of putting metadata into an iterative set of ~identical “containers”
- 19. Skunkworks Hackathons The “containersof containers of containers” idea was elaborated by the belief that we should also reject any solution that required a new API ProgrammableWeb.com already catalogues >16,000 different Web APIs
- 20. Skunkworks Hackathons The “containersof containers of containers” idea was elaborated by the belief that we should also reject any solution that required a new API ProgrammableWeb.com already catalogues >16,000 different Web APIs APIs DO NOT MAKE YOU INTEROPERABLE!
- 21. Skunkworks Hackathons The “containersof containers of containers” idea was elaborated by the belief that we should also reject any solution that required a new API
- 22. Skunkworks Hackathons Are thereexisting standards that are And have the properties of ?
- 24. Uses machine-accessible standardsand representations, following a REST paradigm LDP Useful Features I I + R F + A I Defines HTTP-resolvable URIs for each of these containers Defines the concept of a “Container” - a machine-actionable way to represent repositories, data deposits, data files, data points, and their metadata Uses a widely accepted standard (DCAT) to relate metadata to data → machine- actionable data mining
- 25. Uses machine-accessible standardsand representations, following a REST paradigm LDP Useful Features I I + R F + A I Defines HTTP-resolvable URIs for each of these containers Defines the concept of a “Container” - a machine-actionable way to represent repositories, data deposits, data files, data points, and their metadata Uses a widely accepted standard (DCAT) to relate metadata to data → machine- actionable data mining
- 26. The FAIR Accessor Inincremental detail
- 27. What can wedescribe with FAIR Accessors? FAIR Accessors provide a machine-actionable, structured, REST-oriented way to publish Metadata about a wide range of scholarly “entities”
- 28. What can wedescribe with FAIR Accessors? Warehouses (e.g. EBI) Databases (e.g. UniProt) Repositories (e.g. Zenodo, INRA-URGI Wheat Repo, UniProt) Datasets (e.g. output from a workflow) Research Objects (data a/o workflow a/o results a/o publications) Data “slices” (e.g. the result of a database query) Data Records (e.g. image, excel file, patient clinical record) Other…
- 29. HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIRmetadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Container Resource
- 30. (a “resource” is aURI / URL) HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”?
- 31. HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIRmetadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Container Resource
- 32. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”?
- 33. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? There is a URI for the “Container” (of any of the kinds listed in the previous slide)
- 34. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? Resources are manipulated using the HTTP protocol on the Resource URI For the FAIR Accessor, the only HTTP method we currently require is HTTP GET
- 35. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? What is returned is a document full of metadata richly describing that Container (warehouse, database, dataset, slice, etc.) And a list of Resources (URIs) that represent the contained “things”
- 36. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? Looking more closely at one of those contained things...
- 37. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? The contained thing is a Resource
- 38. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? That Resource can be resolved by HTTP GET
- 39. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? To retrieve a Metadata document describing that resource (e.g. a single record)
- 40. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Which record does this Metadata describe? The foaf:primaryTopic attribute defines this
- 41. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Using the metadata structures defined by DCAT the FAIR Accessor may also tell you how to get the content of the record, and what formats are available
- 42. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? In this case, the record is available in XML format By calling HTTP GET on URL_U2
- 43. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Or in RDF format by calling HTTP GET on URL_U1
- 44. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”?
- 45. Or you mayadd additional layers... Metadata Metadata Metadata Metadata DATA - format 1 DATA - format 2
- 46. Features of theFAIR Accessor 1: There is no API GET Interpret the Metadata Select the desired Resource GET
- 47. Features of theFAIR Accessor 1: There is no API GET Interpret the Metadata Select the desired Resource GET ANY Web agent can explore/index a FAIR Accessor (e.g. Google) An agent that understands globally-accepted vocabularies can explore it “intelligently”
- 48. Features of theFAIR Accessor 49 1: There is no API It’s difficult to get thinner than nothing...
- 49. Features of theFAIR Accessor 2: Identifiers for unidentifi-ed/-able things HTTP GET <FAIR metadata/> This is the ArrayExpress query I did for paper doi:10/1234.56 Results: MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
- 50. Features of theFAIR Accessor 2: Identifiers for unidentifi-ed/-able things HTTP GET <FAIR metadata/> This is the ArrayExpress query I did for paper doi:10/1234.56 Results: MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... Should assist with reproducibility and transparency
- 51. Features of theFAIR Accessor 3: A predictable “place” for metadata PrimaryTopic: record 1A445 Record Metadata... DATA - format 1 DATA - format 2 Different “kinds” of metadata have distinct ontological types, and distinct document structures. There is no ambiguity regarding what the metadata is describing - a repository or a record. Repository metadata MetaRecordURL
- 52. Features of theFAIR Accessor 3: Symmetry & predictable path to citation XXX Part of dataset XXX Metadata... DATA - format 1 DATA - format 2 The record metadata contains an “upward” link to the Repository- level metadata, which should contain license and citation information Repository metadata: Cite: doi:10/8847.384 License: cc-by
- 53. Features of theFAIR Accessor 4: Granularity of Access/Privacy/Security Container Resource HTTP GET <FAIR metadata/> Contains <<184 Records>> Contact Mark Wilkinson For more information about These records
- 54. Features of theFAIR Accessor 4: Granularity of Access/Privacy/Security Container Resource HTTP GET <FAIR metadata/> Contains <<184 Records>> Contact Mark Wilkinson For more information about These records
- 55. Features of theFAIR Accessor Container HTTP GET <FAIR metadata/> Contains MetaRecordResource3 MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:distribution <<NONE>> HTTP GET 4: Granularity of Access/Privacy/Security
- 56. Features of theFAIR Accessor Container HTTP GET <FAIR metadata/> Contains MetaRecordResource3 MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:distribution <<NONE>> HTTP GET 4: Granularity of Access/Privacy/Security
- 57. Container HTTP GET <FAIRmetadata/> Contains MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml HTTP GET Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security
- 58. Container HTTP GET <FAIRmetadata/> Contains MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml HTTP GET Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security
- 59. Features of theFAIR Accessor 4: Granularity of Access/Privacy/Security Thin solution - if it’s private, do nothing! Literally!
- 60. The Real Thing Aworking FAIR Accessor Serving a “Slice” of UniProt
- 61. A real-world scenario... Youare publishing a paper describing the evolution of proteins in the RNA Processing machineries of the fungus Aspergillus nidulans. You want to be a good scholarly publisher interested in transparency and reproducibility So you must describe, in detail, the inclusion/exclusion criteria for selecting proteins for your dataset (today, this is generally done either in the text of the paper, or not at all...)
- 62. The query thatreturns the relevant proteins WHERE { ?protein a up:Protein . ?protein up:organism ?organism . ?organism rdfs:subClassOf taxon:162425 . ?protein up:classifiedWith ?go . ?go rdfs:subClassOf* <http://purl.obolibrary.org/obo/GO_0006396> . bind(replace(str(?protein), "http://purl.uniprot.org/uniprot/", "", "i") as ?id) }
- 63. The query thatreturns the relevant proteins WHERE { ?protein a up:Protein . ?protein up:organism ?organism . ?organism rdfs:subClassOf taxon:162425 . ?protein up:classifiedWith ?go . ?go rdfs:subClassOf* <http://purl.obolibrary.org/obo/GO_0006396> . bind(replace(str(?protein), "http://purl.uniprot.org/uniprot/", "", "i") as ?id) } NCBI Taxonomy: Aspergillus nidulans
- 64. The query thatreturns the relevant proteins WHERE { ?protein a up:Protein . ?protein up:organism ?organism . ?organism rdfs:subClassOf taxon:162425 . ?protein up:classifiedWith ?go . ?go rdfs:subClassOf* <http://purl.obolibrary.org/obo/GO_0006396> . bind(replace(str(?protein), "http://purl.uniprot.org/uniprot/", "", "i") as ?id) } Gene Ontology: RNA Processing
- 65. Create and publisha FAIR Accessor for that query http://linkeddata.systems/Accessors/UniProtAccessor Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
- 66. Create and publisha FAIR Accessor for that query http://linkeddata.systems/Accessors/UniProtAccessor Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... Resolve the URI (in software or in your browser)
- 67. Create and publisha FAIR Accessor for that query Returns a page of metadata (in this example, in RDF) Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
- 69.
- 70. 71 Note that thisMetadata is about ME! I am the creator of this dataset, and may be credited for it.
- 75. Container Resource HTTPGET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
- 76. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET Step down to individual Record metadata
- 77. Step down toindividual Record metadata MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET Software calls HTTP GET on the URL representing the MetaRecord Resource for the desired record in the Container (or just click on it, or type it into your browser)
- 78. <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml The document that is returned
- 80. Note the changein metadata focus! This metadata is about the UniProt Record (not about Mark Wilkinson). The record described in this metadata was created by UniProt, so the citation and authorship information is now THEIRS, not MINE.
- 81. Container Resource Symmetrical Link back upwardto the Accessor Container, for additional metadata
- 82. <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml
- 83. Two ways toretrieve the record - RDF or HTML (in REST-speak, two Representations of that Resource)
- 84. Note that thismetadata record is somewhat more FAIR, than what you can (easily) retrieve from UniProt itself! e.g. the UniProt record does not include the citation or license information - you have to manually surf around the UniProt Web page to find that. So the Accessor makes UniProt’s already notably FAIR data, even more FAIR (with respect to “R”)
- 85. How FAIR arewe now? What does the Accessor give us?
- 86. What we haveachieved We have created a FAIR record for something - i.e. a slice of a database - that was, historically, un-recordable and un-identifiable in any formal way.F F + A F + R Accessors are a standard approach to providing human & machine accessible metadata to facilitate appropriate discovery (contextual, biological), proper usage (license) and proper citation for any kind of data. The discovery, accessibility, and drill-down/up behaviors do not require any novel API, rather simply rely on global Web standards; this allows them to be indexed by existing Web search engines
- 87. What we haveachieved F + AI The metadata itself uses machine-accessible syntaxes, and widely adopted ontologies and vocabularies, thus easily integrates with other metadata A Accessors provide a lightweight means to protect privacy while still providing the maximum degree of transparency possible + Accessors can be static, or dynamic. i.e. we can provide template Accessor file(s) that are edited in Notepad, then published together with the data; or Accessors can dynamically generate their output from code (e.g. layered on a database server)
- 88. So far, wehave focused on FAIR Metadata
- 89. Are there approachesto making the DATA FAIR?
- 90. Making a Plant-relatedResource FAIR FAIR reformatting of the plant component of the Pathogen Host Interaction Database (PHI-base)
- 91. Making a Plant-relatedResource FAIR Dr. Mikel Egaña Aranguren Ontologist Dr. Alejandro Rodríguez González Database Expert Dr. Alejandro Rodríguez Iglesias (PhD student at the time) Rodriguez-Iglesias A., Rodriguez-González A., Irvine AG., Sesma A., Urban M., Hammond-Kosack KE., Wilkinson MD. 2016. Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base. Frontiers in Plant Science 7.
- 92. Extract Transform Load A “Brute Force” approach to FAIRness Requires a ~comprehensive data/semantic model Making a Plant-related Resource FAIR
- 93. The Plant PathogenInteraction Ontology (PPIO) Written in OWL2 Many of the Classes are defined by rich logical axioms Designed for automated classification and enrichment of data through logical reasoning (e.g. if attached to a data stream) Semantic Modeling of Plant Pathogen Interaction Data
- 94. General introduction 95 TheDisease Triangle – Pathogen/Host/Environment http://fyi.uwex.edu/fieldcroppathology/field-crops-fungicide-information/
- 95. General introduction 96 TheDisease Triangle – Pathogen/Host/Environment This concept has evolved over decades of domain-expert thought and discussion Why not use this as the basis for our Semantic model of Pathogenicity?
- 96. The Disease Triangle,Modelled as “Contexts” Interaction Context Environmental Context Host Context Pathogen Context Resulting phenotype
- 97. Interaction Context Phenotype Resistance phenotype Susceptibilityphenotype Phenotypic Process 1. Abnormal growth development phenotype. 2. Color variation phenotype. 3. Tissue disintegration phenotype. 4. Vascular system damage phenotype.
- 98.
- 99.
- 100. The Disease Triangle,Modelled as “Contexts” Interaction Context Environmental Context Host Context Pathogen Context Resulting phenotype
- 101. Environmental Context manually extractedfrom the literature Environmental Context
- 102. Environmental Context manually extractedfrom the literature Environmental Context
- 103. Environmental Context manually extractedfrom the literature Environmental Context
- 104. A pathogen thatenters through the stomata will be more successful in high humidity, and have higher pathogenicity Environmental Context manually extracted from the literature Environmental Context
- 105. The Disease Triangle,Modelled as “Contexts” Interaction Context Environmental Context Host Context Pathogen Context Resulting phenotype
- 106. • Plant-pathogen interactiondata, including: • Resulting phenotypes • Molecular/genetic basis of pathogenicity • Experimental approaches • Provenance information Host Context Pathogen Context
- 107. • 4800 interactions •3300 gene-mutant records • 220 pathogens • 130 hosts • 261 registered diseases • 1700 references Host Context Pathogen Context
- 108. Interaction Context Interaction Context [WT] Interaction Context [mutant] Host Context Host Context Pathogen Context Pathogen Context Description DescriptionProtocolProtocol “Historical observation” “Base state” “reduced virulence” “soft rot” Protocol descriptionCitation PubMed ID “PMID:1234” “gene deletion” Environmental Context Environmental Context Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)
- 109. Interaction Context Interaction Context [WT] Interaction Context [mutant] Host Context Host Context Pathogen Context Pathogen Context Description DescriptionProtocolProtocol “Historical observation” “Base state” “reduced virulence” “soft rot” Protocol descriptionCitation PubMed ID “PMID:1234” “gene deletion” Environmental Context Environmental Context Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)
- 110.
- 111. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A.et al Front. Plant Sci., (2016) http://identifiers.org/*/*
- 112. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A.et al Front. Plant Sci., (2016) rdfs:label
- 113. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A.et al Front. Plant Sci., (2016) http://identifiers.org/*/*
- 114. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A.et al Front. Plant Sci., (2016) http://identifiers.org/*/* KEY MESSAGE: Because we use identifiers.org URIs, and AgroLD does also, we can query our Pathogen Host Interaction database, and DYNAMICALLY RETRIEVE additional information from AgroLD with NO additional effort!!
- 115. Transform PHI-base datainto RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore
- 116. Transform PHI-base datainto RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore (this was a LOT of work!!)
- 117. Transform PHI-base datainto RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore …but are we now FAIR?
- 118. Transform PHI-base datainto RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore …but are we now FAIR? …Not really….
- 119. Findable Accessible Interoperable ReusableX X HTTP GET, SPARQL,open access RDF with published ontologies
- 120. Findable Accessible Interoperable ReusableX X • How wouldyou find this database? • How would you know if anything interesting is in it? • How would you (your machine) find a record? • Who do you cite if you reuse a piece of data? • What are the license conditions? • Can I reuse the data at all?? HTTP GET, SPARQL, open access RDF with published ontologies
- 121. Build a FAIRAccessor http://linkeddata.systems/SemanticPHIBase/Metadata Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic SemPHI #1 dcat:Distribution_1 Source URL_U1 Format rdf+xml dcat:Distribution_2 Source URL_U2 Format HTML HTTP GET
- 122. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic SemPHI #1 dcat:Distribution_1 Source URL_U1 Format rdf+xml dcat:Distribution_2 Source URL_U2 Format HTML HTTP GET Build a FAIR Accessor http://linkeddata.systems/SemanticPHIBase/Metadata The URL of the record in “native” PHI-base
- 123. Container Resource HTTP GET <FAIRmetadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic SemPHI #1 dcat:Distribution_1 Source URL_U1 Format rdf+xml dcat:Distribution_2 Source URL_U2 Format HTML HTTP GET Build a FAIR Accessor http://linkeddata.systems/SemanticPHIBase/Metadata The URL of the (RDF) record in Semantic PHI-base
- 124. This allows usto find Semantic PHI-base based on its Repository-level Metadata “what kind of data does Semantic PHI-base Contain?” “Does it have any information about my gene of interest?” And “drill-down” to a record of interest selected based on its Record Metadata
- 125. But… FAIR Accessorsshould be symmetrical How do I go from data back “upwards” to metadata?
- 126. But… FAIR Accessorsshould be symmetrical How do I go from data back “upwards” to metadata? To allow the retrieval of the Metadata for any piece of data in Semantic PHI Base Use the URL of the FAIR Accessor (Container Resource) as the URL of the “Named Graph” in the triplestore using RDF “Quads” SubjectURI PredicateURI ObjectURI ContextURL Container URL HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
- 127. But… FAIR Accessorsshould be symmetrical How do I go from data back “upwards” to metadata? Container URL HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... To allow the retrieval of the Metadata for any piece of data in Semantic PHI Base Use the URL of the FAIR Accessor (Container Resource) as the URL of the “Named Graph” in the triplestore using RDF “Quads” SubjectURI PredicateURI ObjectURI Container URL
- 128.
- 129. The Brute Forceapproach is… a lot of work! Worthwhile for community-critical resources and databases like AgroLD, UniProt, PHI-base, ChEMBL, etc.
- 130. Is there amore “elegant” & lightweight way to be FAIR?
- 131. FAIR Projection: Providing FAIRData from non-FAIR Data Dynamically
- 132. This is goingto be a bit complicated, but please be patient
- 133. Imagine the data weneed to integrate is in a CSV file in FigShare or Zenodo How do we discover and integrate that data?
- 134. Things we needto do: We need a way to query “opaque” data blobs (like CSV) about their content We need a way to retrieve that content in a FAIR format We need, therefore, to model semantics for that opaque data content We need to model various semantics for that content (one “size” doesn’t fit all!) We need to associate those semantic models with a record or record-sets We need a way to query those semantics determine which “size” fits our req’s We would like to reuse semantic definitions as much as possible We need to do all of this without creating a new API :-)
- 135. Triple Pattern Fragments + RDFMapping Language Ruben Verborgh Ghent University Anastasia Dimou Ghent University
- 136. Triple Pattern Fragments(TPF) A REST interface for requesting/retrieving RDF Triples (from any source) Ruben Verborgh “Slices” of data, from any source, are considered Resources and are therefore represented by a distinct URL: http://some.database.org/dataset?s=___;p=___;o=___ Calling HTTP GET on a TPF URL returns the set of Triples matching {?s, ?p, ?o} PLUS hypermedia instructions and Resource URLs for other relevant slices.
- 137. Triple Pattern Fragments(TPF) A REST interface for retrieving RDF Triples (from any source) Ruben Verborgh For example, the “BMI” column from a patient registry is a Resource with the URL: http://my.registry.org/patients?p=CMO:0000105 (CMO:0000105 = “body mass index””) HTTP GET gives me all BMI triples in the registry, together with other Resource URLs representing other “slices” that might be useful, for example: http://my.registry.org/patients?p=CMO:0000004 (CMO:0000004 = “systolic B.P.”)
- 138. Triple Pattern Fragments(TPF) A REST interface for retrieving RDF Triples (from any source) Ruben Verborgh For example, the “BMI” column from a patient registry is a Resource with the URL: http://my.registry.org/patients?p=CMO:0000105 (CMO:0000105 = “body mass index””) HTTP GET gives me all BMI triples in the registry, together with other Resource URLs representing other “slices” that might be useful, for example: http://my.registry.org/patients?p=CMO:0000004 (CMO:0000004 = “systolic B.P.”)
- 139. We have astandard, RESTful way to request triples from any data source i.e. every slice of every dataset will be considered a distinct Resource → simply call HTTP GET on that Resource to get the Triples
- 140. But... We have noway to know what TPF Resources are available for any given dataset or what those Resources “are” (proteins? genes? patients? articles?)
- 141. RML A way todescribe the structure of an RDF document Anastasia Dimou RML allows us to create models of (meta)data structures “What could this data look like, if it were mapped to RDF?” RML fulfills similar objectives to DCAT Profiles, the Dublin Core Application Profile, and ISO 11179 - Metadata Registries; but has added advantages! http://rml.io/RMLmappingLanguage.html
- 142. Using RML todescribe the structure and semantics of a single Triple Map1 Predicate Object Map Subject Map Object Map ex:Patient Record subjectMap template “http://example.org/patient/{id}” predicate ex:has Variant Map2 Subject Map2 SO:0000694 (“SNP”) template “http://identifiers.org/dbsnp/{snp}” T H E M O D E L
- 143. Using RML todescribe the structure and semantics of a single Triple Map1 Predicate Object Map Subject Map Object Map ex:Patient Record subjectMap template “http://example.org/patient/{id}” predicate ex:has Variant Map2 Subject Map2 SO:0000694 (“SNP”) template “http://identifiers.org/dbsnp/{snp}” T H E M O D E L We call this a “Triple Descriptor” These are used to describe the structure of data “slices” in which all Triples have the same structure
- 144. T H E M O D E L Using RML todescribe the structure and semantics of a single Triple Map1 Predicate Object Map Subject Map Object Map ex:Patient Record subjectMap template “http://example.org/patient/{id}” predicate ex:has Variant Map2 Subject Map2 SO:0000694 (“SNP”) template “http://identifiers.org/dbsnp/{snp}” Patient:123 rdf:type ex:Patient Record snp: rs0020394 ex:hasVariant rdf:type ex:Patient Record The Data
- 145. Where are wenow? TPF - A standard, RESTful way to request Triples Triple Descriptors - A standard way to describe the structure and meaning of a Triple
- 146. Where are wenow? TPF - A standard, RESTful way to request Triples Triple Descriptors - A standard way to describe the structure and meaning of a Triple We need a way to associate these with each other We need a way to associate these with a dataset or record
- 147. Luckily, we havealready solved this!
- 148. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET The FAIR Accessor can do this Using the metadata structures defined by DCAT the FAIR Accessor also tells you how to get the content of the record, and what formats are available
- 149. If we considerthe TPF Resource URL to be just another DCAT Distribution, we get... <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml
- 150. <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml If we consider the TPF Resource URL to be just another DCAT Distribution, we get... URL representing the Triple Pattern Fragment Resource
- 151. If we considerthe TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPFrag_URL_1 format rdf+xml Model: Triple_Desc_URL <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL
- 152. <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL_1 Format rdf+xml Model: Triple_Desc_URL If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor HTTP GET on that URL returns:
- 153. <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor Record + TPF Server + RML Model = FAIR Projector
- 154. If we considerthe TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor HTTP GET on TPF_URL returns rdf+xml triples from Record R That look like Interoperability without Brute Force <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL
- 155. I hear youobjecting… I skipped something important!!! We still have not defined a way to CREATE these triples
- 156. <FAIR metadata/> foaf:primaryTopic RecordR dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL I hear you objecting… I skipped something important!!! How does this return Triples? We still have not defined a way to CREATE these triples
- 157. Sadly, there isno magic wand to create interoperability
- 158. Sadly, there isno magic wand to create interoperability Someone has to write the TPF server that converts the data Interoperability will never come “for free” (because semantics will never come “for free”)
- 159. However, there arereasons for optimism! 1. Researchers transform data anyway to integrate it - this is a daily routine in most bioinformatics labs 2. For the most common file formats (e.g. CSV or Excel), there are RML-based tools to automate the RDF transformation; simply create an RML model of what you want, and ask the tool to covert the file. 3. Investing time into creating an RML model is more FAIR than ad hoc “re-useless” brute-force transformation. When you create a FAIR Projector for your own data transformation needs, it is reusable!
- 160. However, there arereasons for optimism! AND 4. RML Triple Descriptors are very simple (one triple!) so we can also templatize their construction creating a FAIR Projector is quite easy in many cases! 5. Citations Citations Citations! FAIR Accessors/Projectors are FAIR objects - You can get credit if other people use your Projector for their analyses
- 161. Summary of FAIRProjectors FAIR Projectors provide a discoverable and standardized REST interface to retrieve interoperable data, and its interoperable metadataF+I+R A + I I FAIR Projectors can convert non-FAIR data into FAIR data, or can change the structure, URL format, or semantics of existing FAIR data sources FAIR Projectors can be deployed over, and provide a common interface to: - Static Data Deposits, in any format, anywhere - Databases - Triplestores - Certain (common) types of Web Services R+++ Triple Descriptors are FAIR entities, intended for reuse, & None of this required a new API
- 162. Siri… I need dataabout the expression of the Oryza dwarf-1 gene under high salt conditions. Please find that data, regardless of location and format If possible, please reformat it automatically to match my local dataset Also, please collect the citation information for each piece of data If the data is not under an open access license, or if any of the data is behind a firewall or paywall, please provide me the contact information of the data owner so that I can ask them for a copy. The (near!) future of FAIR
- 163. Thanks to: Michel Dumontier- Stanford Center for Biomedical Informatics Research, Stanford, California. Ruben Verborgh – Ghent University – imec, Ghent, Belgium Luiz Olavo Bonino da Silva Santos - Dutch Techcentre for Life Sciences, Utrecht, The Netherlands - Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. Tim Clark - Department of Neurology, Massachusetts General Hospital Boston MA and Harvard Medical School, Boston, MA, USA Morris A. Swertz - Genomics Coordination Center and Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands Fleur D.L. Kelpin - Genomics Coordination Center and Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands Alasdair J. G. Gray - Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK Erik A. Schultes - Department of Human Genetics, Leiden University Medical Center, The Netherlands Erik M. van Mulligen - Department of Medical Informatics, Erasmus University Medical Center Rotterdam, The Netherlands Paolo Ciccarese - Perkin Elmer Innovation Lab, Cambridge MA and Harvard Medical School, Boston MA, USA Mark Thompson - Leiden University Medical Center, Leiden, The Netherlands Jerven T. Bolleman - Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland
- 164. Thanks to myformer lab members… I MISS YOU!!! Dr. Mikel Egaña Aranguren Ontologist Dr. Alejandro Rodríguez González Database Expert Dr. Alejandro Rodríguez Iglesias (PhD student at the time)
- 165. Funding for MarkWilkinson from: Fundacion BBVA and the UPM Isaac Peral programme, and the Spanish Ministerio de Economía y Competitividad grant number TIN2014-55993-R. Additional support for FAIR Skunkworks members comes from: European Union funded projects ELIXIR-EXCELERATE (H2020 no. 676559), ADOPT BBMRI-ERIC (H2020 no. 676550) CORBEL (H2020 no. 654248) Netherlands Organisation for Scientific Research (Odex4all project) Stichting Topconsortium voor Kennis en Innovatie High Tech Systemen en Materialen (FAIRdICT project) BBMRI-NL RD-Connect and ELIXIR (Rare disease implementation study FP7 no. 305444).
- 166. You are notonly welcome to share and reuse this presentation... ...You are encouraged to! http://tinyurl.com/IBC-FAIR
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Editor's Notes
- #19 http://www.freedigitalphotos.net/images/Business_people_g201-Businessman_shaking_hand_p88735.html
- #96 One of the cornerstones in plant pathology is the disease triangle , which states that for a disease to occur, three factors must be present: a virulent pathogen, a susceptible host and a propitious environment (case a.). If one of the components is manipulated somehow, the disease severity will be affected (rest of the cases). Thus, host-pathogen interactions are contextually-dependent on internal (e.g. genotype) and external factors (e.g. environment).
- #97 One of the cornerstones in plant pathology is the disease triangle , which states that for a disease to occur, three factors must be present: a virulent pathogen, a susceptible host and a propitious environment (case a.). If one of the components is manipulated somehow, the disease severity will be affected (rest of the cases). Thus, host-pathogen interactions are contextually-dependent on internal (e.g. genotype) and external factors (e.g. environment).
- #98 Therefore, for every Interaction Context, there must be an Environment Context, a Host Context and a Pathogen Context. Moreover, the outcome of the interaction can result on a resistance response or the development of the disease. These outcome come in form of an observable phenotype. I will now describe how all these elements were modeled.
- #99 Significant effort was made towards modeling phenotypes in PPIO, both pathogenic and plant phenotypes. An infection of a susceptible host will usually result in an altered phenotype, produced through some physiological infective process. We model these using two classes. The first one, PHENOTYPE is the semantic representation of the outcome of the interaction, that is, a resistance response or the development of the disease. The RESISTANCE PHENOTYPE class contains a set of subclasses representing the different resistance mechanisms, such as HR, cell wall reinforcement or production of antimicrobial compounds. The disease situation, modeled as SUSCEPTIBILITY PHENOTYPE class, was divided into four general subclasses: ABNORMAL GROWTH DEVELOPMENT PHENOTYPE COLOR VARIATION PHENOTYPE TISSUE DISINTEGRATION PHENOTYPE VASCULAR SYSTEM DAMAGE PHENOTYPE We also created the PHENOTYPIC PROCESS class to represent the symptoms plants exhibit due to alterations at the cellular level. Every phenotypic response in PPIO is deeply axiomatized and connected to one or more of the SUSCEPTIBILITY PHENOTYPE subclasses, according to the symptomatology of the type of lesion.
- #100 This example shows how the CHLOROTIC STRIPE LESION subclass was axiomatically modeled as resulting in a COLOR VARIATION PHENOTYPE subclass., showing the connection between the two classes I just mentioned. In some cases, the interconnection between these two classes was more complex. In this example, CANKER subclass was classified and connected with two Phenotype subclasses, such as the Vascular system damage phenotype and theTissue disintegration phenotype. At this point, and following best ontology and FAIR practices, we decided to make use of the Plant Trait Ontology. This platform, part of the Gramene Project, contains semantic descriptions of physiological, biochemical and molecular plant traits. By importing this into our own ontology, we made possible the interconnection of every phenotypic response and the proper PTO traits affected by it, as in these two examples. As a result, the interconnection of the Susceptibility phenotype, Phenotypic process and the PTO classes assured a solid way to express the disease outcome situation for every interaction.
- #101 This example shows how the CHLOROTIC STRIPE LESION subclass was axiomatically modeled as resulting in a COLOR VARIATION PHENOTYPE subclass., showing the connection between the two classes I just mentioned. In some cases, the interconnection between these two classes was more complex. In this example, CANKER subclass was classified and connected with two Phenotype subclasses, such as the Vascular system damage phenotype and theTissue disintegration phenotype. At this point, and following best ontology and FAIR practices, we decided to make use of the Plant Trait Ontology. This platform, part of the Gramene Project, contains semantic descriptions of physiological, biochemical and molecular plant traits. By importing this into our own ontology, we made possible the interconnection of every phenotypic response and the proper PTO traits affected by it, as in these two examples. As a result, the interconnection of the Susceptibility phenotype, Phenotypic process and the PTO classes assured a solid way to express the disease outcome situation for every interaction.
- #102 Therefore, for every Interaction Context, there must be an Environment Context, a Host Context and a Pathogen Context. Moreover, the outcome of the interaction can result on a resistance response or the development of the disease. These outcome come in form of an observable phenotype. I will now describe how all these elements were modeled.
- #103 By the time the PPIO was created, we found no trace of semantic databases storing environmental data that allowed automatic extraction, so we were limited to extract the initial knowledge from the literature. We selected moisture, nutrient availability, soil traits and temperature as main subclasses of the Environmental parameter class, modeled here using Protégé. Relation between infection and environment is shown in this simple example. A combination of the environmental annotation of HIGH HUMIDITY (that favours stomatal opening) and a pathogen annotation like ENTRY THROUGH STOMATA could be passed to a logical reasoner which could automatically generate the novel assumption that a particular pathogen would be more succesful under those conditions. In the future, we expect to achieve a rich axiomatiaztion of all these parameters to provide powerful reasoning behaviors.
- #104 By the time the PPIO was created, we found no trace of semantic databases storing environmental data that allowed automatic extraction, so we were limited to extract the initial knowledge from the literature. We selected moisture, nutrient availability, soil traits and temperature as main subclasses of the Environmental parameter class, modeled here using Protégé. Relation between infection and environment is shown in this simple example. A combination of the environmental annotation of HIGH HUMIDITY (that favours stomatal opening) and a pathogen annotation like ENTRY THROUGH STOMATA could be passed to a logical reasoner which could automatically generate the novel assumption that a particular pathogen would be more succesful under those conditions. In the future, we expect to achieve a rich axiomatiaztion of all these parameters to provide powerful reasoning behaviors.
- #105 By the time the PPIO was created, we found no trace of semantic databases storing environmental data that allowed automatic extraction, so we were limited to extract the initial knowledge from the literature. We selected moisture, nutrient availability, soil traits and temperature as main subclasses of the Environmental parameter class, modeled here using Protégé. Relation between infection and environment is shown in this simple example. A combination of the environmental annotation of HIGH HUMIDITY (that favours stomatal opening) and a pathogen annotation like ENTRY THROUGH STOMATA could be passed to a logical reasoner which could automatically generate the novel assumption that a particular pathogen would be more succesful under those conditions. In the future, we expect to achieve a rich axiomatiaztion of all these parameters to provide powerful reasoning behaviors.
- #106 By the time the PPIO was created, we found no trace of semantic databases storing environmental data that allowed automatic extraction, so we were limited to extract the initial knowledge from the literature. We selected moisture, nutrient availability, soil traits and temperature as main subclasses of the Environmental parameter class, modeled here using Protégé. Relation between infection and environment is shown in this simple example. A combination of the environmental annotation of HIGH HUMIDITY (that favours stomatal opening) and a pathogen annotation like ENTRY THROUGH STOMATA could be passed to a logical reasoner which could automatically generate the novel assumption that a particular pathogen would be more succesful under those conditions. In the future, we expect to achieve a rich axiomatiaztion of all these parameters to provide powerful reasoning behaviors.
- #107 Therefore, for every Interaction Context, there must be an Environment Context, a Host Context and a Pathogen Context. Moreover, the outcome of the interaction can result on a resistance response or the development of the disease. These outcome come in form of an observable phenotype. I will now describe how all these elements were modeled.
- #108 Although we already had modeled both the Host and Pathogen conceptual entities, there was a need for modeling a set of additional concepts around them. The previously mentioned Pathogen-Host Interaction Database (PHI-base) was used as a source of the data we needed to model. This is an important resource for plant sciences , and while not limited to plants only, it captures the data relevant to thousands of plant/pathogen interactions, the resulting phenotypes, in many cases information about the molecular/genetic basis of pathogenicity, experimental approaches and provenance information. After obtaining the permission of the PHI-base consortium, we extracted the plant-portion data, containing around 4800 different interactions, 3300 gene records, interactions, 225 pathogens, 132 hosts, with 261 registered diseases and 1693 references.
- #109 Although we already had modeled both the Host and Pathogen conceptual entities, there was a need for modeling a set of additional concepts around them. The previously mentioned Pathogen-Host Interaction Database (PHI-base) was used as a source of the data we needed to model. This is an important resource for plant sciences , and while not limited to plants only, it captures the data relevant to thousands of plant/pathogen interactions, the resulting phenotypes, in many cases information about the molecular/genetic basis of pathogenicity, experimental approaches and provenance information. After obtaining the permission of the PHI-base consortium, we extracted the plant-portion data, containing around 4800 different interactions, 3300 gene records, interactions, 225 pathogens, 132 hosts, with 261 registered diseases and 1693 references.
- #110 For each of these interactions, we actually created two contexts: an interaction context regarding the WT situation and another context where the pathogen genetic context is altered. Both these two contents contained an Enviromental, a Host and a Pahtogen context, a Description and a Protocol class. Description class was aimed to represent the literal phenotypic descriptions per se. The Protocol class describes the experimental approaches undertaken to study the pathogen genetic background in each case. Since PHI Base only contains genetic and experimental data regarding the mutant panorama, the WT context was modeled with basic information. The Mutant context situation, on the other hand, was deeply modeled, adding all the information PHI-Base contained, like the mutant-derived phenotypic outcome, the experimental approach chosen and its provenance information (including the PubMed article ID). During this modelling phase we reuse class names and property names from a wide variety of third-party ontologies, like the Relation Ontology, EDAM Ontology, Experimental Factor Ontology or Schema.org. One of the most useful information PHI-Base provided was the pathogen genetic framework, with information about 3000 gene records.
- #111 For each of these interactions, we actually created two contexts: an interaction context regarding the WT situation and another context where the pathogen genetic context is altered. Both these two contents contained an Enviromental, a Host and a Pahtogen context, a Description and a Protocol class. Description class was aimed to represent the literal phenotypic descriptions per se. The Protocol class describes the experimental approaches undertaken to study the pathogen genetic background in each case. Since PHI Base only contains genetic and experimental data regarding the mutant panorama, the WT context was modeled with basic information. The Mutant context situation, on the other hand, was deeply modeled, adding all the information PHI-Base contained, like the mutant-derived phenotypic outcome, the experimental approach chosen and its provenance information (including the PubMed article ID). During this modelling phase we reuse class names and property names from a wide variety of third-party ontologies, like the Relation Ontology, EDAM Ontology, Experimental Factor Ontology or Schema.org. One of the most useful information PHI-Base provided was the pathogen genetic framework, with information about 3000 gene records.
- #112 Extensive modeling of this data was also performed, that began by connecting the Pathogen Context and the Gene class via a conceptual Allele class. The Gene class was further annotated with additional information PHI-Base provided, like… As a summary, by combining literature data, third-party ontologies and web resources like PHI-Base around a central plant pathology paradigm, we constructed the first semantic platform that fully describes the plant-pathogen-environment interaction knowledge domain, achieving our key goal of plant pathology knowledge modelling.
- #113 Extensive modeling of this data was also performed, that began by connecting the Pathogen Context and the Gene class via a conceptual Allele class. The Gene class was further annotated with additional information PHI-Base provided, like… As a summary, by combining literature data, third-party ontologies and web resources like PHI-Base around a central plant pathology paradigm, we constructed the first semantic platform that fully describes the plant-pathogen-environment interaction knowledge domain, achieving our key goal of plant pathology knowledge modelling.
- #114 Extensive modeling of this data was also performed, that began by connecting the Pathogen Context and the Gene class via a conceptual Allele class. The Gene class was further annotated with additional information PHI-Base provided, like… As a summary, by combining literature data, third-party ontologies and web resources like PHI-Base around a central plant pathology paradigm, we constructed the first semantic platform that fully describes the plant-pathogen-environment interaction knowledge domain, achieving our key goal of plant pathology knowledge modelling.
- #115 Extensive modeling of this data was also performed, that began by connecting the Pathogen Context and the Gene class via a conceptual Allele class. The Gene class was further annotated with additional information PHI-Base provided, like… As a summary, by combining literature data, third-party ontologies and web resources like PHI-Base around a central plant pathology paradigm, we constructed the first semantic platform that fully describes the plant-pathogen-environment interaction knowledge domain, achieving our key goal of plant pathology knowledge modelling.
- #116 Extensive modeling of this data was also performed, that began by connecting the Pathogen Context and the Gene class via a conceptual Allele class. The Gene class was further annotated with additional information PHI-Base provided, like… As a summary, by combining literature data, third-party ontologies and web resources like PHI-Base around a central plant pathology paradigm, we constructed the first semantic platform that fully describes the plant-pathogen-environment interaction knowledge domain, achieving our key goal of plant pathology knowledge modelling.