Mikel egana itbam_2010_ogo_system

Aug 18, 2010Download as ODP, PDF0 likes376 views

The document describes the OGO (Orthologs and Genes Ontology) system, which provides a semantic query interface for exploring information about ortholog genes and genetic diseases. It allows users to formulate complex queries about orthologs and diseases without needing SPARQL syntax. An example query and its results are shown, finding the ortholog genes of the gene that causes prostate cancer in rats. Future plans include adding more reasoning capabilities and integrating with additional biomedical ontologies and standards.

A semantic query interface for the OGO platform José Antonio Miñarro-Giménez (jose.minyarro@um.es) Mikel Egaña Aranguren, Ph.D. (mikel.egana.aranguren@gmail.com) Francisco García-Sánchez, Ph.D. (frgarcia@um.es) Jesualdo Tomás Fernández-Breis, Ph.D. (jfernand@um.es) Faculty of Computer Science University of Murcia Spain ITBAM (DEXA) Bilbo 2010 http://tinyurl.com/35amhn6

Overview Orthologs Information about orthologs and diseases OGO system A semantic query interface for the OGO system Sample query

Ortholog sequences http://www.stanford.edu/group/pandegroup/folding/education/h.htm

Orthologs and genetic diseases Homologene KOG Inparanoid OrthoMCL Online Mendelian Inheritance in Man (OMIM) Gene 1 Disease Gene 2 Orthologs

OGO ontology: imported ontologies Gene Ontology (OBOF): molecular function, biological process and cellular component of gene products Evidence Codes Ontology (Candidate OBOF): GO annotations evidence codes OBO Relationship Types (Candidate OBOF): Gene product participates in some (molecular function or biological process) Gene product located in some cellular component NCBI taxonomy: organisms classification

Interfaces of the OGO system Keyword based querying Semantic querying

Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

$Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ? @prefix ncbi: <http://um.es/ncbi.owl>. @prefix ogo: <http://miuras.inf.um.es/ontologies/OGO.owl>. SELECT ?Gene_0 ?Genetic_disease_1 WHERE { ?Gene_0 ogo:fromSpecies ncbi:NCBI_10116 ?Genetic_disease_1 ogo:Name ?literal_4 . FILTER (regex(?literal_4,"Prostate cancer, susceptibility to")) . ?Genetic_disease_1 ogo:causedBy ?Gene_2 . ?Cluster_of_Orthologous_genes_3 ogo:hasOrthologous ?Gene_2 . ?Cluster_of_Orthologous_genes_3 ogo:hasOrthologous ?Gene_0 . }$

Query grammar Query::= "SELECT" ListVar (WhereClause)? ListVar::=Var (Var)* WhereClause::="WHERE {" ConditionClause (ConditionClause)* "}" ConditionClause::=[VarCondition | LiteralCondition] "." VarCondition::=[Var | Individual] Property [Var | Individual] LiteralCondition::=[Var | Individual] Property [Var | Individual] "." "FILTER (regex (" Var "," Literal "))" Var -> This term represents a variable in the query which can be matched to any concept or individual in the ontology. Individual -> This term represents a concept or individual identied by an URI in the ontology. Property -> This term represents a relationship or property identied by an URI in the ontology. Literal -> This term represents any data value dened by the user.

Future plans OWL reasoning for querying (OWL 2 QL?) Pellet Integrity Constraint Validator (Pellet ICV): OWL as schema language for RDF (CWA) Check the gathered information More bio-ontologies Clinical archetypes for querying (ISO 13606): exchange of ortholog/disease information in a standard biomedical research setting

Conclusions Orthologs and diseases: new hypotheses OGO provides a resource for exploiting such combined information Semantic query interface: “Complex” queries easily (No SPARQL syntax) http://miuras.inf.um.es/~ogo/

Acknowledgements Spanish Ministry for Science and Education (grant TSI2007-66575-C02-02) Comunidad Autónoma de la Región de Murcia (grant BIO-TEC 06/01-0005) Fundación Séneca, Servicio de Empleo y Formación (grant 07836/BPS/07)

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provenance of microarray experimentsHelena Deus

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This document discusses representing the provenance of microarray gene expression experiments in RDF. It outlines how microarray experiments work and the importance of provenance data for understanding results. It then describes a bottom-up approach to developing a provenance model for microarray data, including four types of provenance - institutional, experimental context, data analysis, and dataset descriptions. An example gene list and queries are provided. The model represents provenance as RDF to enable SPARQL querying. Future work includes integrating provenance from scientific workflows and spreadsheet results.

2012 03 01_bioinformatics_ii_les1Prof. Wim Van Criekinge

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The document provides information about a bioinformatics course including the syllabus, topics, and schedule. The course focuses on using relational databases to manage and analyze large biological datasets. Topics include relation databases, web application development, genome browsers, text mining, and systems biology. The schedule lists the topics to be covered each class over 12 weeks from March to May.

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ICAR2016 TAIR talkDonghui Li

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provenance of microarray experimentsHelena Deus

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2012 03 01_bioinformatics_ii_les1Prof. Wim Van Criekinge

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Mikel egana itbam_2010_ogo_system

1. A semantic query interface for the OGO platform José Antonio Miñarro-Giménez (jose.minyarro@um.es) Mikel Egaña Aranguren, Ph.D. (mikel.egana.aranguren@gmail.com) Francisco García-Sánchez, Ph.D. (frgarcia@um.es) Jesualdo Tomás Fernández-Breis, Ph.D. (jfernand@um.es) Faculty of Computer Science University of Murcia Spain ITBAM (DEXA) Bilbo 2010 http://tinyurl.com/35amhn6

2. Overview Orthologs Information about orthologs and diseases OGO system A semantic query interface for the OGO system Sample query

3. Ortholog sequences http://www.stanford.edu/group/pandegroup/folding/education/h.htm

4. Ortholog sequences Trait Trait

5. Orthologs and genetic diseases Homologene KOG Inparanoid OrthoMCL Online Mendelian Inheritance in Man (OMIM) Gene 1 Disease Gene 2 Orthologs

6. OGO system

7. OGO ontology

8. OGO ontology: imported ontologies Gene Ontology (OBOF): molecular function, biological process and cellular component of gene products Evidence Codes Ontology (Candidate OBOF): GO annotations evidence codes OBO Relationship Types (Candidate OBOF): Gene product participates in some (molecular function or biological process) Gene product located in some cellular component NCBI taxonomy: organisms classification

9. Imported ontologies: OWL punning

10. OGO ontology: mappings to OMIM

11. Implementation of the OGO system

12. Interfaces of the OGO system Keyword based querying Semantic querying

13. Semantic interface

14. Semantic interface

15. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

16. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

17. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

18. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

19. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

20. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ? @prefix ncbi: <http://um.es/ncbi.owl>. @prefix ogo: <http://miuras.inf.um.es/ontologies/OGO.owl>. SELECT ?Gene_0 ?Genetic_disease_1 WHERE { ?Gene_0 ogo:fromSpecies ncbi:NCBI_10116 ?Genetic_disease_1 ogo:Name ?literal_4 . FILTER (regex(?literal_4,"Prostate cancer, susceptibility to")) . ?Genetic_disease_1 ogo:causedBy ?Gene_2 . ?Cluster_of_Orthologous_genes_3 ogo:hasOrthologous ?Gene_2 . ?Cluster_of_Orthologous_genes_3 ogo:hasOrthologous ?Gene_0 . }

21. Sample query Ortholog genes of the gene that causes prostate cancer on Rattus norvegicus ?

22. Query grammar Query::= "SELECT" ListVar (WhereClause)? ListVar::=Var (Var)* WhereClause::="WHERE {" ConditionClause (ConditionClause)* "}" ConditionClause::=[VarCondition | LiteralCondition] "." VarCondition::=[Var | Individual] Property [Var | Individual] LiteralCondition::=[Var | Individual] Property [Var | Individual] "." "FILTER (regex (" Var "," Literal "))" Var -> This term represents a variable in the query which can be matched to any concept or individual in the ontology. Individual -> This term represents a concept or individual identied by an URI in the ontology. Property -> This term represents a relationship or property identied by an URI in the ontology. Literal -> This term represents any data value dened by the user.

23. Future plans OWL reasoning for querying (OWL 2 QL?) Pellet Integrity Constraint Validator (Pellet ICV): OWL as schema language for RDF (CWA) Check the gathered information More bio-ontologies Clinical archetypes for querying (ISO 13606): exchange of ortholog/disease information in a standard biomedical research setting

24. Conclusions Orthologs and diseases: new hypotheses OGO provides a resource for exploiting such combined information Semantic query interface: “Complex” queries easily (No SPARQL syntax) http://miuras.inf.um.es/~ogo/

25. Acknowledgements Spanish Ministry for Science and Education (grant TSI2007-66575-C02-02) Comunidad Autónoma de la Región de Murcia (grant BIO-TEC 06/01-0005) Fundación Séneca, Servicio de Empleo y Formación (grant 07836/BPS/07)

Editor's Notes

#2: The OGO system ... Presentation URL Creative commons attribution non commercial share alike
#4: Orthologs are homolog sequences (they share a common ancestor) that diverged by an speciation event
#5: Orthologs can be used to generate hypotheses. For example, if frog alpha and chicken alpha are ortholog genes, and it is known that frog alpha is involved in a certain trait (e.g. a disease), then it is likely that chicken alpha is also involved in or related to such trait, in chicken Therefore, the information about orthologs is very important in biomedical research, since they show new research paths for human diseases with a genetic cause
#6: Unfortunately, information about orthologs and diseases is scattered and it is difficult to combine
#7: The OGO system provides a resource for accesing the ortholog/diseases combined information in a precise way. The OGO system is an OWL KB, in which the OGO ontology provides the schema and the information regarding orthologs and diseases is stored in instances, with relationships between them The OGO ontology is also used as a guide for the user to build queries The system is accessed with keywords or SPARQL The pipeline is executed periodically (Mappings, information checking)
#8: OGO ontology (KB schema and querying)
#9: Imported ontologies (GO, ECO, RO) reuse existing semantics for querying, as we will see when I describe the queries OBOF: Wealth of quality reusable semantics of the biodomain GO: Member ECO, RO: Candidates
#10: Not detailed Classes as values (OBO format) Future DL
#11: Pipeline
#12: JENA allows to store OWL in a MySQL database, and to access it with SPARQL
#13: The OGO system has two interfaces: Keyword based interface (by disease/by orthologs): not very expressive but fast Semantic interface (next)
#14: The semantic interface is more expressive than the keyword based interface. However, as SPARQL is difficult to use by biologists, the semantic interface provides a graphical interface for creating queries, that, later, are translated into SPARQL It should be noted that this does not allow to use the whole expressivity of SPARQL, but a considerable part of it (see grammar)
#15: In order to define the query, we can select concepts from the OGO ontology, and add any requirements, also using the OGO ontology We can exploit the imported ontologies for querying: GO, ECO, NCBI The defined query is translated into SPARQL and executed against the KB
#16: Whole process First we select the variables that we are interested in from the OGO ontology. In this case, Gene and Genetic disease (i.e, we want to retrieve Genes and Genetic diseases) The imported ontologies can be exploited (GO, ECO, NCBI) for querying
#17: Then we add requirements, also using the OGO ontology (And Imported ontologies ). We can use the selected variables or new ones. We can delete/edit requirements
#18: We edit a requirement by using the OGO ontology (to add new variables and values) or by using the already defined variables NCBI (imported, like GO, and ECO) for providing values for the requirement
#19: We add the finished requirement to the the query
#20: We can add as many requirements as we want
#21: Finally, the query is translated into SPARQL and executed against the KB
#22: Results
#23: The expressivity of the query is limited by the grammar
#26: YOGY already does this, however, redundant results by resource, instead of gene centric, i.e.same gene in different resources OGO ontology is used to check the consistency of the info Less expressivity in SPARQL: no OPTIONAL

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Mikel egana itbam_2010_ogo_system

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