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OmicsDI
Omics discovery index
Rafael C Jimenez
How we can find a publication?
How we can find a dataset?
Aims
• Facilitate discovery of high-quality ‘omics’
datasets (genomics, proteomics, transcriptomics
and metabolomics).
• Provide added value not just integration
• Provide the infrastructure to register and
integrate data repositories and their datasets.
http://www.omicsdi.org
15 repositories
90,880 datasets
3,840 diseases
2,781 tissues
4,605 species
Datasets search
Similarity visualization
Metadata SimilarityBiological Similarity
Dataset metadata format
Mandatory Fields:
• Dataset Id
• Dataset Title
• Publication date
• Submitter information (Name, Affiliation)
• Original URL
Recommended Fields:
• Description/Abstract
• Sample and Data Protocols
• PubMed Id
• Organism, Tissue, Disease.
Additional Fields:
• Protein Id (Ensembl or Uniprot)
• Metabolite Id (ChEMBL)
• More…
https://github.com/BD2K-DDI/specifications/blob/master/docs/schema/fileds.md
OmicsDI XMLDatabases
EBI Search Indexer
INDEXING
ENGINE
EBI CLUSTER
RESTWS
SEARCH
ENGINE
End points:
• Statistics
• Datasets
RESTFUL WS DATABASE
WEB APP
SEARCH
STATISTICS
TAGGING
DDIApp
Enrichment
Annotation
Metadata processing
• Input data Validation
https://github.com/BD2K-DDI/xml-validator
• Metadata enrichment
https://github.com/BD2K-DDI/ddi-annotation
• Identifier mapping
ie. DOI to puimde IDs
• Ontologies mapping
ie. Text to NCBI taxonomy
• Ontology enrichment
ie. Disease synonymous and parent terms (annotator)
• Analysis of dataset entries
ie. Overlap of biological entries between datasets
Thanks for your attention

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