Real World Applications of OWL

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A presentation for the March 2012 Protege Short Course

http://protege.stanford.edu/shortcourse/protege-owl/201203/index.html

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  • So slide 1 is the workflow we have for getting data into the two repositories. We take publicly submitted data, often accompanying a publication, and we also have a pipeline importing data from GEO which we re-annotate. Data is ran through some scripts to check minimum QC then it is manually curated which then goes into ArrayExpress. The second part of that pipeline is that a subset of that data, selected based on array design type, is re-annotated against ontology terms. We use the gene annotations plus ontology terms to integrate and analyse on a per gene per condition level and summarise the diff expression for each gene vs condition - this is Atlas.Second slide is showing the gene expression atlas. you can explore the data using the ontology (far right), it expands based on the tree and on synonym (annotation properties). The view you see is based on the ontology tree.Third slide is ArrayExpress which is not annotated with EFO URIs but here we use the ontology to drive query expansion simply using it as a vocabulary. A search for cancer would expand on all subtypes and perform a Lucene query across the data for anything matching this text. This will change in near future as we are aiming to annotate both against the ontology.
  • The RDF representing the evaluation of the input hypothesis is linked to both the hypothesis AND the data used to evaluate the hypothesis
  •  Workflow for generating background annotation sets for enrichment analysis: First, we start with a corpus of PubMed articles identified in manually curated GO annotations. These curated annotations provide gene-to-article associations. Next, we annotate the titles and abstracts of each article with ontology terms using the NCBO Annotator service. Terms associations can be expanded based on inferred hierarchical relationships. Finally, the gene-to-article associations are linked with the curated article-to-term associations to obtain a list of gene-to-term associations. The resulting term frequencies provide a background set for enrichment analysis
  • Real World Applications of OWL

    1. 1. Real World Applications of OWL Michel Dumontier, Ph.D. Associate Professor of Bioinformatics Department of Biology, School of Computer Science, Institute of Biochemistry, Carleton University Ottawa Institute of Systems Biology Ottawa-Carleton Institute of Biomedical Engineering Professeur Associé, Université Laval Visiting Associate Professor, Stanford University1 Protege Short Course::Dumontier:March 2012
    2. 2. Ontologies in Use • Knowledge Capture (Rightfield) • Formalization and Verification (SNOMED-CT) • Consistency Checking (SBML Harvester) • Classification (Phosphatases, Compounds) • Semantic Annotation (Array Express/ Gene Expression Atlas, Semantic Assistant) • Query Formulation (Array Express/ Gene Expression Atlas) • Query Answering (KUPD) • Search & co-occurence (gopubmed) • Semantic Assistant • Hypothesis Testing (HyQue) • Disease Similarity and Model Organism prediction (phenomeBLAST) • Function Prediction (genemania)2 Protege Short Course::Dumontier:March 2012
    3. 3. Knowledge Capture Rightfield K.Wolstencroft, S.Owen, M.Horridge, O.Krebs, W.Mueller, JL. Snoep, F.Preez, C.Goble RightField: Embedding ontology annotation in spreadsheets. Bioinformatics (2011), May 20113 Protege Short Course::Dumontier:March 2012
    4. 4. Formalization SNOMED-CT • SNOMED-CT (Clinical Terms) ontology • used in healthcare systems of more than 15 countries, including Australia, Canada, Denmark, Spain, Sweden and the UK • also used by major US providers, e.g., Kaiser Permanente • ontology provides common vocabulary for recording clinical data • 395036 classes4 Protege Short Course::Dumontier:March 2012
    5. 5. SNOMED-CT • Pattern based knowledge capture • need training and an information system to implement5 Protege Short Course::Dumontier:March 2012
    6. 6. SNOMED - verification • Kaiser Permanente extending SNOMED to express, e.g.: – non-viral pneumonia (negation) – infectious pneumonia is caused by a virus or a bacterium (disjunction) – double pneumonia occurs in two lungs (cardinalities) • This is easy in SNOMED-OWL – but reasoner failed to find expected subsumptions, e.g., that bacterial pneumonia is a kind of non-viral pneumonia • Ontology highly under-constrained: need to add disjointness axioms (at least) – virus and bacterium must be disjoint - Ian Horrocks OWL2 tutorial6 Protege Short Course::Dumontier:March 2012
    7. 7. SNOMED • Adding disjointness led to surprising results – many classes become inconsistent, e.g., percutanious embolization of hepatic artery using fluoroscopy guidance • Cause of inconsistencies identified as class groin – groin asserted to be subclass of both abdomen and leg – abdomen and leg are disjoint – modelling of groin (and other similar “junction” regions) identified as incorrect - Ian Horrocks OWL2 tutorial7 Protege Short Course::Dumontier:March 2012
    8. 8. Consistency Checking Formalization of SBML annotations into OWL ontologies • Biomodels contains hundreds of quantitative models • SBML is an XML-based format for specifying models and their parameters • Models and their components are being semantically annotated • Use the ontologies to validate the assertions Integrating systems biology models and biomedical ontologies. Hoehndorf R, Dumontier M, Gennari JH, Wimalaratne S, de Bono B, Cook DL, Gkoutos GV. BMC Syst Biol. 2011 Aug 11;5:124.8 Protege Short Course::Dumontier:March 2012
    9. 9. Additional annotations are specified using the Resource Description Framework (RDF) <species metaid="_525530" id="GLCi" Implicit subject compartment="cyto" and xml attributes initialConcentration="0.097652231064563"> <annotation> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax- The annotation ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" element stores the xmlns:vCard="http://www.w3.org/2001/vcard-rdf/3.0#" xmlns:bqbiol="http://biomodels.net/biology-qualifiers/" RDF xmlns:bqmodel="http://biomodels.net/model-qualifiers/"> subject <rdf:Description rdf:about="#_525530"> <bqbiol:is> <rdf:Bag> <rdf:li predicate rdf:resource="urn:miriam:obo.chebi:CHEBI%3A4167"/> <rdf:li rdf:resource="urn:miriam:kegg.compound:C00031"/> </rdf:Bag> </bqbiol:is> object </rdf:Description> </rdf:RDF> </annotation> </species>The intent is to express that the species represents a substance composed of glucose moleculesWe also know from the SBML model that this substance is located in the cytosol and with a (initial) concentration of 0.09765M9
    10. 10. For each model annotation, we make a commitment to what it represents OWL Axiom: M SubClassOf: represents some MaterialEntity Conversion rule: a Model annotated with class C represents: If C is a SubClassOf MaterialEntity then M SubClassOf: represents some C If C is a SubClassOf Function then M SubClassOf: represents some (has-function some C) If C is a SubClassOf Process then M SubClassOf: represents some (has-function some (realized- by only C))10
    11. 11. 11
    12. 12. Model verification After reasoning, we found 27 models to be inconsistent reasons 1. our representation - functions sometimes found in the place of physical entities (e.g. entities that secrete insulin). better to constrain with appropriate relations 2. SBML abused – e.g. species used as a measure of time 3. Incorrect annotations - constraints in the ontologies themselves mean that the annotation is simply not possible12
    13. 13. Finding inconsistencies with axiomatically enhanced ontologies ATPase activity (GO:0004002) is a Catalytic activity that has Water and ATP as input, ADP and phosphate as output and is a part of an ATP catabolic process. To this, we add: • GO: ATP + Water the only inputs (universal quantification) • ChEBI: Water, ATP, alpha-D-glucose 6-phosphate are all different (disjointness) • “ATP” input to “ATPase” reaction, which is annotated with ATPase activity. The species “ATP”, however, is mis- annotated with Alpha-D-glucose 6-phosphate (CHEBI:17665), not with ATP. • Unsatisfiable -> curation error in BIOMD0000000176 and BIOMD0000000177 models of anaerobic glycolysis in yeast.13
    14. 14. Classification: Phosphotases • Bioinformaticians use tools to identify functional domains (e.g., InterProScan) • Tools simply show the presence of domains - they do not classify proteins • Experts classify proteins according to domain arrangements - the presence and number of each domain is important PhosphaBase: an ontology-driven database resource for protein phosphatases. Wolstencroft KJ, Stevens R, Tabernero L, Brass A. Proteins. 2005 Feb 1;58(2):290-4.14 Protege Short Course::Dumontier:March 2012
    15. 15. Phosphatase Functional Domains15 Protege Short Course::Dumontier:March 2012
    16. 16. Defining Protein Phosphatases • Necessary and sufficient conditions are stipulated using EquivalentClass axioms • A protein phosphatase is exactly a protein that consists of exactly one transmembrane domain and contains at least one phosphotase domain ProteinPhosphatase EquivalentTo: Protein AND hasDomain 1 transMembraneDomain AND hasDomain min 1 PhosphataseCatalyticDomain16 Protege Short Course::Dumontier:March 2012
    17. 17. More precise class expressions can be formulated for subtypes Inclusion of universal quantifier now restricts the domains to only the types listed R2A EquivalentTo: Protein AND hasDomain 2 ProteinTyrosinePhosphataseDomain AND hasDomain 1 TransmembraneDomain AND hasDomain 4 FibronectinDomains AND hasDomain 1 ImmunoglobulinDomain AND hasDomain 1 MAMDomain AND hasDomain 1 Cadherin-LikeDomain AND hasDomain only (TyrosinePhosphataseDomain OR TransmembraneDomain OR FibronectinDomain OR ImnunoglobulinDomain OR Clathrin-LikeDomain OR ManDomain)17 Protege Short Course::Dumontier:March 2012
    18. 18. Describing chemical functional groups in OWL-DL for the classification of chemical compounds methyl group hydroxyl group Ethanol Knowledge of functional Functional groups describe groups is important in chemical reactivity in terms of chemical synthesis, atoms and their connectivity, and pharmaceutical design and exhibits characteristic chemical lead optimization. behavior when present in a compound. N Villanueva-Rosales, M Dumontier. 2007. OWLED, Innsbruck, Austria.18 Protege Short Course::Dumontier:March 2012
    19. 19. Describing Functional Groups in DL R group O R H HydroxylGroup: CarbonGroup that (hasSingleBondWith some (OxygenAtom that hasSingleBondWith some HydrogenAtom)19 Protege Short Course::Dumontier:March 2012
    20. 20. Fully Classified Ontology 35 FG20 Protege Short Course::Dumontier:March 2012
    21. 21. And, we define certain compounds Alcohol: OrganicCompound that (hasPart some HydroxylGroup)21 Protege Short Course::Dumontier:March 2012
    22. 22. Organic Compound Ontology 28 OC22 Protege Short Course::Dumontier:March 2012
    23. 23. Question Answering: Classes as self-contained queries • Query PubChem, DrugBank and dbPedia23 Protege Short Course::Dumontier:March 2012
    24. 24. Querying Kidney and Urinary Knowledge Base and Ontology Query: What are the genes involved in Proteins transport expressed in Proximal Tubule Epithelial Cell? Entre gene KUPO Ontology Gene X GO:0054426 PT epithelial cell go:biological_process MA:00345 rdfs:label Gene Y ro:part_of kupo:002444 Higgings Dataset Proximal tubule DT epithelial cell MA:000345 MA:00456 Gene X rdfs:label kupo:expressed_in Distal tubule ro:part_of kupo:004672 Gene Y MA:00456 kupo:expressed_in24 Protege Short Course::Dumontier:March 2012
    25. 25. Semantic Annotation and Query ArrayExpress Curation Curation >250,000 Assays ATLAS AE/GEO acquire >10,000 Re-annotate & summarize experiments Ontologically Modeling Sample Variables in Gene Expression Data malone@ebi.ac.uk25 Protege Short Course::Dumontier:March 2012
    26. 26. ontology-based data exploration Query for Cell adhesion genes in all „organism parts‟ „View on EFO‟ Ontologically Modeling Sample Variables in Gene Expression Data malone@ebi.ac.uk26 Protege Short Course::Dumontier:March 2012
    27. 27. Ontology-based query expansion for ArrayExpress Archive @ www.ebi.ac.uk/arrayexpress27 Protege Short Course::Dumontier:March 2012
    28. 28. Search and Co-Occurrence28 Protege Short Course::Dumontier:March 2012
    29. 29. Semantic Assistant services relevant for the users current task are offered directly within a desktop application. This approach relies on ontology-described semantic web services to provide external natural language processing (NLP) pipelines Leverage of OWL-DL axioms in a Contact Centre for Technical Product Support Alex Kouznetsov, Bradley Shoebottom, René Witte, Christopher JO Baker. OWLED 2010.29 Protege Short Course::Dumontier:March 2012
    30. 30. Plug-in for Open Office Client30 Protege Short Course::Dumontier:March 2012
    31. 31. • HyQue helps construct and evaluate (automatically obtain support for) hypotheses using formalized background knowledge and data using the Semantic Web • HyQue makes it possible to develop a reliability model around data based on our scientific expectations of corroborating evidence Callahan A, Dumontier M, Shah NH. HyQue: evaluating hypotheses using Semantic Web technologies. J Biomed Semantics. 2011 May 17;2 Suppl 2:S3. Callahan A, Dumontier M. Evaluating scientific hypotheses using the SPARQL Inferencing Notation. Extended Semantic Web Conference (ESWC 2012). Heraklion, Crete. May 27-31, 2012. Accepted.31 Protege Short Course::Dumontier:March 2012
    32. 32. Hypothesis h1: • simple event- e1 (Gal4p induces expression of GAL1) based expression h2: e2 (Gal3p induces expression of GAL2 • conjunctive hypothesis – e3 AND Gal4p induces expression of GAL7) must satisfy two h3: expressions e4 (Gal4p induces expression of GAL7 e5 AND Gal80p inhibits production of Gal4p • conjunctive when GAL3 is over-expressed hypothesis with e6 AND Gal80p induces expression of GAL7) conditional expression32 Protege Short Course::Dumontier:March 2012
    33. 33. HYQUE ARCHITECTURE Callahan A, Dumontier M, Shah NH. HyQue: evaluating hypotheses using Semantic Web technologies. J Biomed Semantics. 2011 May 17;2 Suppl 2:S3. Callahan A, Dumontier M. Evaluating scientific hypotheses using the SPARQL Inferencing Notation. Extended Semantic Web Conference (ESWC 2012). Heraklion, Crete. May 27-31, 2012. Accepted.33 Protege Short Course::Dumontier:March 2012
    34. 34. Rule-based assessment of evidence • „induce‟ rule (maximum score: 5): – Is event negated? GO:0010628 • If yes, subtract 2 – Is logical operator „induce‟? CHEBI:36080 • If yes, add 1; if no, subtract 1 – Is agent of type „protein‟ or „RNA‟? • If yes, add 1; if of type „gene‟, subtract 1 – Is target of type „gene‟? SO:0000236 • If yes, add 1; if no, subtract 1 – Does agent have known „transcription factor activity‟? • If yes, add 1 GO:0003700 – Is event located in the „nucleus‟? • If yes, add 1; if no, subtract 1 GO:000563434 Protege Short Course::Dumontier:March 2012
    35. 35. Linked Open Results : from hypothesis to evidence35 Protege Short Course::Dumontier:March 2012
    36. 36. Literature-Based Enrichment Analysis • Enrichment analysis on terms extracted using a target ontology for associated articles. Enabling enrichment analysis with the Human Disease Ontology. Paea LePendu, , Mark A. Musen, Nigam H. Shah. Journal of Biomedical Informatics. Volume 44, Supplement 1, December 2011, Pages S31–S3836 Protege Short Course::Dumontier:March 2012
    37. 37. 37 Protege Short Course::Dumontier:March 2012
    38. 38. Phenotype-based predictions Phenotypes can be used as a substrate to cluster similar diseases, identify potential model systems, predict potential disease- treating drugs or their adverse events, drug repurposing, etc Robert Hoehndorf, Paul N. Schofield and Georgios V. Gkoutos. PhenomeNET: a whole- phenome approach to disease gene discovery. Nucleid Acids Research, 2011. Linking pharmgkb to phenotype studies and animal models of disease for drug repurposing. Hoehndorf R, Oellrich A, Rebholz-Schuhmann D, Schofield PN, Gkoutos GV. Pac Symp Biocomput. 2012:388-99. CK Chen, CJ Mungall, GV Gkoutos et al. MouseFinder: candidate disease genes from mouse phenotype data. Human Mutation 201238 Protege Short Course::Dumontier:March 2012
    39. 39. Tetralogy of Fallot Phenotype ontologies should contain descriptions of morphological, behavioural, physiological, developmental characteristics Human Phenotype Ontology OMIM39 Protege Short Course::Dumontier:March 2012
    40. 40. Compare Diseases based on their Phenotypes Comparison using Weighted Jaccard – uses information content for a phenotype regarding genotype or disease40 Protege Short Course::Dumontier:March 2012
    41. 41. Inferring equivalent phenotypes by reasoning over OWL ontologies human „overriding aorta [HP:0002623]‟ EquivalentTo: „phenotype of‟ some („has part‟ some („aorta [FMA:3734]‟ and „overlaps with‟ some „membranous part of interventricular septum [FMA:7135]‟) mouse „overriding aorta [MP:0000273 ]‟ EquivalentTo: „phenotype of‟ some („has part‟ some („aorta [MA:0000062]‟ and „overlaps with‟ some „membranous interventricular septum [MA:0002939]‟ Uberon super-anatomy ontology provides inter-species mappings „aorta [FMA:3734]‟ EquivalentTo: „aorta [MA:0002939]‟ „membranous part of interventricular septum [FMA:3734]‟ EquivalentTo: „membranous interventricular septum [MA:0000062] Thus, „overriding aorta [HP:0002623] EquivalentTo:„overriding aorta[MP:0000273]‟41 Protege Short Course::Dumontier:March 2012
    42. 42. Identifying potential mouse models for human diseases Quantitative ROC Analysis prediction against curated models yields 0.89 AUC Prediction of Tetralogy of Fallot added by MGI42 Protege Short Course::Dumontier:March 2012
    43. 43. Conclusion • OWL has come of age and can be used in an increasing number of scientific investigations and applications • OWL applications cover knowledge capture, formalization, verification, classification, semantic annotation, query formulation, query answering, search, hypothesis testing and prediction43 Protege Short Course::Dumontier:March 2012

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