Ontology application and use at the encode dcc
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This document discusses the use of ontologies in annotating metadata for the ENCODE project. It describes how ontologies provide a consistent way to describe biological concepts and facilitate identifying related data. The document outlines how biosample, treatment, and assay metadata are annotated using ontologies like Uberon, Cell Ontology, and Experimental Factor Ontology. It also gives examples of how ontologies allow flexible searching of metadata, such as finding all heart-related tissues. Future directions discussed include using additional ontologies and ontology-based validation of data.
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Ontology application and use at the encode dcc
- 1. Ontology application and use at the ENCODE DCC Venkat Malladi Data Wrangler, ENCODE DCC Department of Genetics Stanford University School of Medicine Venkat Malladi ENCODE DCC
- 2. Overview Venkat Malladi ENCODE DCC Metadata Model Ontologies Search Future directions Intro to ENCODE and the DCC
- 3. What is ENCODE? Venkat Malladi ENCODE DCC Modified from PLoS Biol 9-e1001046,2011 (M. Pazin) Approximately ~30 different assays
- 4. Role of the Data Coordination Center Venkat Malladi ENCODE DCC Production labs Analysis groups Genome Browser ENCODE portal (DCC) Data files Metadata DCC DCC Integrative websites Scientific community Role: Data generation Data organization Data access Tasks: Perform assays Data processing & validation Web-based searches Perform analyses Data file storage Data downloads Validate data Metadata curation Submit data files Submit metadata
- 5. Challenge: Find common biosamples from data generated by two consortia Venkat Malladi ENCODE DCC 356 terms http://encodeproject.org/ENCODE/cellTypes.html Projects are internally consistent….. 314 terms GEO characteristics: common_name, tissue_type, cell_type, lines
- 6. Simple text match Venkat Malladi ENCODE DCC 360 terms Cell type … but only 3 biosample names match exactly between projects 314 terms GEO IMR90 PBMC Th17
- 7. Metadata annotation using Ontologies
- 8. An ontology is a set of words and relationships … … All relationships must be true. Venkat Malladi ENCODE DCC nucleuschromosome mitochondrial chromosome mitochondrion cell Parent term Child term part_of part_of part_of part_of is_a part_of X
- 9. An ontology is a set of words and relationships. Need true relationships because inferences can be based upon them. Venkat Malladi ENCODE DCC nucleuschromosome mitochondrial chromosome mitochondrion cell Parent term Child term part_of part_of part_of part_of is_a part_of X part_of X part_of http://www.geneontology.org/GO.ontology.relations.shtml True False
- 10. Why use ontologies? Venkat Malladi ENCODE DCC Reason 1: Consistent way of describing biological concepts Reason 2: Consistency of language facilitates identification of related data easily. Reason 3: Consistency in data analysis because relationships between terms provide flexibility of grouping while everyone uses the same set of metadata
- 11. What metadata is annotated with ontologies? Venkat Malladi ENCODE DCC 1. the biological sample serving as input (Biosample) 1. the reagents and conditions applied to the biological input (Treatment) 1. the set of methods and conditions to survey the biological input (Assay)
- 12. Venkat Malladi ENCODE DCC
- 13. Biosample ontologies Venkat Malladi ENCODE DCC 1. Uber anatomy ontology (Uberon) - structure, location and heterogenous mixture of cells 1. Cell Ontology (CL) - primary cells or stem cells 1. Experimental Factor Ontology (EFO) - no direct corresponding anatomical structure or physiological cell type
- 14. Venkat Malladi ENCODE DCC
- 15. Challenge: Find all heart-related tissues? Venkat Malladi ENCODE DCC Heart_OC HCF HCFaa HCM Others? Fetal Heart Heart Right Atrium Right Ventricle Others?
- 16. Searching ENCODE metadata Venkat Malladi ENCODE DCC
- 17. Ontology driven search Venkat Malladi ENCODE DCC
- 18. Future directions Venkat Malladi ENCODE DCC • Additional ontologies • Ontology- based data validations
- 19. Additional ontologies Venkat Malladi ENCODE DCC • Protein Ontology (PRO,http://pir.georgetown.edu/pro/pro.shtml) o transforming growth factor beta-1 (human)— PR:P01137 • EDAM Ontology (EDAM, http://edamontology.org) o FASTQ—format:1930, BAM—format:2572 o sequence alignment—data:0863
- 20. Ontology based validations Venkat Malladi ENCODE DCC
- 21. Acknowledgments Venkat Malladi ENCODE DCC Nikhil Podduturi, Laurence Rowe, Forrest Tanaka Esther Chan, Jean Davidson, Venkat Malladi, Cricket Sloan, J. Seth Strattan Eurie Hong, Mike Cherry (PI), Jim Kent (co-PI), Ben Hitz Brian Lee, Stuart Miyasato, Matt Simison, Zhenhua Wang, Marcus Ho Data Wranglers Software Engineers QA, administration, biocuration National Institute of General Medical Sciences of the United States AQ1215 National Institutes of Health (GM10331601); U41 grant from National Human Genome Research Institute at the U.S. National Institutes of Health (HG006992)