Enabling knowledge management in the Agronomic Domain
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The document discusses knowledge management within the agronomic domain, emphasizing the need for effective data integration to bridge the gap between biological data and knowledge. It highlights the significance of ontologies and semantic web technologies in enhancing data interoperability and analysis, as well as initiatives like the Agronomic Linked Data project aimed at integrating diverse plant-related datasets. Future developments include expanding coverage to support comparative analyses and facilitating user-driven research in molecular plant biology.
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Enabling knowledge management in the Agronomic Domain
- 1. Enabling 油knowledge 油management 油 in 油the 油Agronomic 油Domain 油 Pierre 油Larmande 油 Ins-tute 油of 油Computa-onnal 油Biology 油 油 (IBC) 油 Pierre.larmande@ird.fr 油
- 2. Mul7-足scale 油omics 油integra7on 油 BIG 油DATA 油 Research 油areas 油 Work鍖ow 油management 油 Seman7c 油web 油
- 3. Outline ≒ Data integration challenges in the Life Sciences" ≒ Ontologies/ Semantic Web Technologies" ≒ Agronomic Linked Data project" ≒ NGS Big Data project"
- 4. Data landscape in the Life Sciences ≒ The availability of biological data has increased" ≒ Advancements in:" ≒ computational biology" ≒ genome sequencing" ≒ high-throughput technologies " ≒ Integrative approaches are necessary to understand the functioning of biological systems"
- 5. ≒ Lack of effective approaches to integrate data that has created a gap between data and knowledge" ≒ Need for an effective method to bridge gap between data and underlying meaning" ≒ Harvest the power of overlaying different data sets" Data integration challenges
- 6. ≒ Todays Web content is suitable for human consumption" ≒ Collection of documents" ≒ the existence of links that establish connections between documents" ≒ Low on data interoperability and lacks semantics." Todays Web
- 7. ≒ Ontologies are formal representations of knowledge - de鍖nitions of concepts, their attributes and relations between them." ≒ To integrate data, improve machine interoperability and data analysis required a conceptual scaffold." ≒ Ontological terms used across databases" ≒ provide cross-domain common entry points in the description." ≒ An array of ontologies are being used to bring structured integration of various datasets." ≒ The Open Biomedical Ontologies (OBO) initiative:" ≒ serves as an umbrella for well structured orthogonal ontologies." ≒ Ontologies represented in OBO format and OWL" Ontologies
- 8. Semantic Web Technology ≒ An extension of the current Web technologies." ≒ Enables navigation and meaningful use of digital resources." ≒ Support aggregation and integration of information from diverse sources." ≒ Based on common and standard formats."
- 9. Resource Description Framework (RDF) ≒ Framework for representing information about resources on the Web" ≒ Provides a labeled connection between two resources" ≒ Uses Unique Resource Identi鍖ers (URI)" ≒ Statements take the form of triples:" Subject 油 Predicate 油 Object 油 <Gene_A> 油 <codes_for> 油 <Protein_A> 油 RDF 油Triple 油
- 10. ≒ Combining the triples results in a directed, labeled graph." <Gene_A> 油 <Protein_A> 油 <has_func7on> 油 <BP_A> 油 <MF_A> 油 <Gene_X> 油 <regulates> 油
- 11. SPARQL ≒ Language which allows querying RDF models (graphs) " ≒ Powerful, 鍖exible" ≒ Its syntax is similar to the one of SQL"
- 12. Agronomic Linked Data (AgroLD) ≒ Semantic web based system that captures knowledge pertaining to plant data" ≒ Aim:" ≒ Capability to answer complex real life questions" ≒ Ef鍖cient information integration / retrieval" ≒ Easy extensibility"
- 13. AgroLD Phase I ≒ AgroLD will be developed in phases " ≒ Phase I: includes data on Oryza sps. and Arabidopsis thaliana! ≒ SPARQL endpoint: http://bioportal.lirmm.fr:8081/test/ " AgroLD 油
- 14. ≒ Integrates information from:" ≒ Ontologies: Gene Ontology, Plant Ontology, Plant Trait Ontology, Plant Environment Ontology, Crop Ontology" ≒ Other information sources: " ≒ Gene/Protein information: GOA, Gramene, Tair, OryzaBase, UniPort " ≒ QTL information: Gramene" ≒ Pathway information: RiceCyc, AraCyc" ≒ Germplasm information: Oryza Tag Line" ≒ Homology prediction: GreenPhyl" Information in AgroLD
- 15. Biological process Cellular Component Molecular Function Interaction Gene 油 Taxon Protein 油 protein 油 Modification Pathway has_par7cipant 油 contains 油 has_func7on 油 has_agent 油 acts_on 油 is_member_of 油 codes_for 油 occurs_in 油 has_source 油 protein 油 Target Gene Knowledge representation in AgroLD
- 16. Future directions ≒ Phase II: having both wider and deeper coverage to promote comparative analysis" ≒ Include varied data types gene expression data, protein-protein interaction, Transcription factor- target gene " ≒ Developing methods to aid the process of hypotheses generation - e.g. inference rules." ≒ Pluggable with work鍖ow platforms e.g.: Galaxy, OpenAlea (VirtualPlants)." ≒ Engage with biologists to mobilise user-pull:" ≒ Develop real world use cases studying the molecular mechanism of panicle differentiation in rice"
- 17. Gigwa: 油Genotype 油Inves7gator 油for 油Genome 油 Wide 油Analyses 油
- 18. Aims 油of 油Gigwa 油 ≒ Manage 油genomic, 油transcriptomic 油and 油 genotyping 油data 油resul-ng 油from 油NGS 油analyses 油 ≒ Handle 油large 油VCF 油鍖les 油to 油鍖lter, 油query 油and 油 extract 油data 油 ≒ Provide 油a 油web 油user 油interface 油to 油make 油the 油 system 油accessible 油for 油all 油 油
- 19. Main 油features 油 o Based 油on 油NoSQL 油technology 油 o Handles 油VCF 油鍖les 油(Variant 油Call 油Format) 油and 油annota-ons 油 o Supports 油mul-ple 油variant 油types: 油SNPs, 油InDels, 油SSRs, 油SV 油 o Powerful 油genotyping 油queries 油 油 o Easily 油scalable 油with 油MongoDB 油sharding 油 o Transparent 油access 油
- 20. Demo 油 油Database 油selec7on 油 Mul--足database 油with 油 restricted 油access 油
- 21. Demo 油 油datatypes 油selec7on 油 Several types of selection: Variant search is restricted to the selected reference sequences and subset of individuals
- 22. Demo 油-足 油Queries 油 Predefined queries
- 23. Demo 油 油Filters 油selec7on 油 Several 油鍖lters 油
- 24. Demo 油 油browsing 油results 油
- 25. Acknowledgements 油 Dr. Patrick Valduriez! Dr. Clement Jonquet Dr. Manuel Ruiz! Guilhem Semp辿r辿! Alexis DereeperGautier Sarah Dr. Aravind Venkatesan Florian Philippe Contact: 油 油pierre.larmande@ird.fr 油 油