Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
AgBase Shane Burgess, Fiona McCarthy Mississippi State University 1. Who we are • Ontology users: – Experimentalists: model our own functional genomics data sets – Tool developers: develop ontology-based tools for functional modeling – Ontology training: train researchers in the use of ontologybased tools • Ontology developers: – Burgess: involved in development of GO immune function – McCarthy: involved in development of GO viral process terms… – AgBase biocurators: GO & PO new term requests and changes during biocuration • Ontology contributors: – funded to provide GO annotations for chicken, agricultural animals, maize, cotton… – funded to provide PO annotations for maize & cotton… 2. Why we use ontologies? • Structure: ability to organize data • Ability to capture data as information sources grow. • Tools: ability to model high throughput data sets – summarizing function – functional enrichment analysis – hypothesis-testing • Comparative biology: use of ontological data to understand differences between species 3. Ontology Tools we are using at AgBase • None • Ontology contributors: – Initially used excel! – Developed in house biocuration interface for annotation • Ontology developers: – Mostly minor development so we only use online ontology browser tools (e.g., QuickGO, AmiGO) 4. Roadblocks • How to annotate increasing number of sequences from new sequencing technologies? – – – – No literature Short sequences – similarity/motif finding not effective Noncoding RNAs Move to tissue/phenotype ontologies to capture data? • Visualization – How to view functional data from multiple ontologies – Expression data, functional data and genome browser? – How to link gene products to pathways and phenotypes? 5. Vision • Need to employ multiple ontologies to get comprehensive functional modeling – cross-ontology queries – rapidly populating other functional ontologies (where is the “IEA pipeline” equivalent?) – enrichment analysis tools that handle multiple ontologies • Manual biocuration – more effective at targeting manual effort towards “high importance” genes – moving annotation data to new species (pathogens, fungus, plants, marine invertebrates…) 6. Collaborators? • Type of person or resource who could help? • Where are the people driving the need?