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Annotating Experimental Records using Ontologies

Annotating Experimental Records using Ontologies. Olga Giraldo , Unal de Colombia/CIAT Jael Garcia, 3 Universität der Bundeswehr Alexander Garcia, UAMS. Motivation and Research Question. Knowledge -based approach to managing laboratory information

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Annotating Experimental Records using Ontologies

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  1. Annotating Experimental Records using Ontologies Olga Giraldo, Unal de Colombia/CIAT Jael Garcia, 3Universität der Bundeswehr Alexander Garcia, UAMS

  2. Motivation and Research Question • Knowledge-based approach to managing laboratory information • it combines elements from the Semantic Web (SW), e.g. ontologies supporting organization and classification, with elements from Social Tagging Systems, e.g.collaboration, ad-hoc organization strategies. • How can we semantically annotate laboratory records? • How can we facilitate the coexistence of laboratory notebooks and electronic laboratory records?

  3. Motivation and Research Question • Easy to use, highly portable, easy to share, low cost… • Great artifacts for supporting design • Legal requirement Mutis Marie Curie da Vinci

  4. Research Question • How can we facilitate the coexistence of laboratory notebooks and electronic laboratory records? • How can we semantically annotate laboratory records?

  5. Our Approach • Documents should be able to “know about” their own content for automated processes to “know what to do” with them. Semantics….

  6. Materials and Methods • Our scenario: supporting the annotation of experimental data for some of the processes routinely run at the Center for International Tropical Agriculture (CIAT) biotechnology laboratory • 15 laboratory notebooks together with their corresponding electronic records, e.g.XLS files, outputs from lab equipment, etc. • 10 biologists • Direct non-intrusive observation: 6 months • Ontology and prototype development: iterative and collaborative process • Existing ontologies

  7. Results • Data types • Rhetorical structure • Ontologies • Orchestration of ontologies • Tags and ontologies • Lessons

  8. Results • Data Types • Manuscript • Digital • Digital data with manuscript annotations

  9. Results • Manuscript • Lists • To-dos • How-tos (protocols) • Incomplete results • Dates • Formulas • Electronic paths • Sources for information (URLs)

  10. Results • Digital • Photos • Lists • Incomplete results • Protocols • Figures • Sequences

  11. Results • Digital + Manuscript • Digital files, print-outs, tagged with manuscript information.

  12. Results • We identified the rhetorical structure implicit in those laboratory notebooks we studied • And the metadata describing such structure

  13. Rhetorical structure: Header, Body. Title (DC) Header: metadata describing a lab notebook Creator (DC/AgMes) Notes (AgMes) Date of creation (DC) Laboratory notebook number (M4L) Date of finalization (M4L) Languaje (DC) Samples: DNA, RNA, whole plant, etc. (OBI, CHEBI, PO) Materials & Methods, experimental design Materials & Methods: Samples, Reagents, Assays, Equipment and supplies. Lab Notebook Body: metadata describing an experimental activity Project (OBI/AGROVOC) Date (DC) Reagents: buffer, dNTP mix (CHEBI, M4L) Laboratory procedure (M4L) Page number (M4L) Assay: extraction DNA, PCR, gel electrophoresis (OBI, M4L). Recorded by (M4L) Purpose (M4L) Equipment & supplies: freezer, centrifuge, shaker, glove, etc. (OBI, PEO, SEP, SNOMED, BIRNLex M4L). Protocol (OBI) Security measurements (M4L) Comments (BioPortal, NCIt, SNOMED) Outcome (NCIt) Experimental design Experimental design: (OBI, M4L)

  14. DNA Extraction We focused on: DNA extraction, PCR and Electrophoresis

  15. A typical process in a plant biotechnology laboratory Mechanical pulverization of plant material

  16. Results • M4L: our ontology for the experimental processes we studied • Based on OBI. • Terms proposed to OBI: 197, including new terms plus terms from other ontologies • Other terms will be proposed to other ontologies, e.g. ChEBI, GO, PO

  17. Results • We have structured the descriptive layers by reusing and extending existing ontologies. • For supporting the annotation within our scenario we have identified three main layers, namely: • i) that related to the document itself, • ii) the annotation layer, and • iii) that related to the experiment.

  18. Results • Orchestration of ontologies: Annotation Ontology The Annotation Ontology is a vocabulary for performing several types of annotation - comment, entities annotation (or semantic tags), textual annotation (classic tags), notes, examples, erratum... - on any kind of electronic document (text, images, audio, tables...) and document parts. AO is not providing any domain ontology but it is fostering the reuse of the existing ones for not breaking the principle of scalability of the Semantic Web.

  19. Selector (304,507) (360,618) aos:init aos:end rdfs:SubClassOf aof:onDocument ImageSelector InitEndCornerSelector rdf:type ao:context aof:annotates Document rdfs:SubClassOf rdf:type Topic Annotation Qualifier ANNOT1 GenBank: AB005238 ao:hasTopic moat:tagMeaning name Provenance pav:createdBy pav:createdOn Partial sequence on psy promoter http://www.tags4lab.org/foaf.rdf#olga.giraldo June 1, 2010 ann:body tags:name MOAT rdf:type foaf:Person moat:Tag rdf:type moat:hasMeaning aoex:hasMoatMeaning moat:Meaning Annotation Definition ANNOT2 rdf:type rdfs:SubClassOf rdf:type aof:annotates Document http://www.ncbi.nlm.nih.gov/pubmed/12520345

  20. Results • The AO is structuring the semantic annotation as well as the tags generated by users. • In this way we are supporting complex SPARQL queries involving several ontologies, for instance: • Retrieve from the eLabBook the pages tagged by Tim Andrews or Lisa Watson with the tags rice and iron for which there is a LIMS data entry”

  21. Concluding Remarks • Although several ELNs have been proposed and replacing paper-based records has been a consistent trend for several years, the technology has not yet been widely adopted; Laboratory Information Management Systems (LIMS) in combination with paper-based laboratory notebooks continue to be commonly used; particularly in academic environments.

  22. Concluding Remarks • Sharing and organizing information happens on a concept basis • researchers studying genes involved in iron transport share information with those who undertake nutritional studies assessing the effects of iron intake in human populations • Clustering information based on concepts

  23. Concluding Remarks • Simple tagging mechanisms proved to be valuable resources for organizing information • Cloud of tags were used as TOCs • Tags were also used to support a quick view of laboratory pages • Tags tend to stabilize over time • Tags were a valuable resource of terms and evidence (use cases) for those terms

  24. Concluding Remarks • Time is difficult to model • Incremental prototyping and participatory design were key –community engagement • Limitations in the technology: • Tablets, electronic pen, ipad first generation, now motorola XOOM • Browser compatibility • Laboratory notebooks look like specialized wikis

  25. Future Work • Focus on one technology: Android OS • Semantic LIMS • Support the whole cycle (LIMS record—notebook—machine generated data) • Automatic annotation of machine generated data • Adopt minimal amounts of information • Adopt techniques from Personal Information Management approaches • Look more like a wiki

  26. Acknowledgments • John Bateman, Oscar Corcho, Joe Tohme, Cesar Montana, Alberto Labarga • The CIAT biotech lab

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