L clarke faang_dcc_isag_2017_compress
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FAANG DCC plenary at #ISAG2017
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L clarke faang_dcc_isag_2017_compress
- 1. How to find and provide FAANG Data The FAANG Data Coordination Centre Laura Clarke Vertebrate Data Coordination www.ebi.ac.uk @laurastephen
- 2. Value of Metadata Data Access Metadata Standards Validation tools FAANG Data availability Support
- 3. Tara Oceans •2 ½ year expedition •210 sampling stations •Standardized measurements •Genetic •Morphological •Physico-Chemical Good metadata enables great science
- 4. Good metadata enables great science
- 5. HipSci •750 iPSC lines •Healthy and rare disease donors •Extensive genomic and epigenomic characterization •All lines and data available to community Good metadata enables great science
- 6. H Kilpinen et al. Nature 546, 370–375 (2017) doi:10.1038/nature22403 Good metadata enables great science
- 7. The FAANG Data Coordination Centre •Supporting Submission •Ensuring high quality data description •Making the data accessible •Providing consistent analysis products
- 8. Findable • Global persistent identifier • Rich metadata • Store metadata in registries Accessible • Resolvable identifiers • Metadata persists • Machine and human access Interoperable • Open data format • Modelled with FAIR compliant vocabularies • Reference external data Reusable • Rich metadata • Clear license • Provenance Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship Authors. Nature Scientific Data 3, 1–15 (2016). DOI: 10.1038/sdata.2016.18 Alasdair J G Gray Heriot-Watt University Ensuring the data is FAIR
- 9. • Needs • Well structured • Consistent naming • Specific descriptions • Enables • Aggregation • Integration • Tracking Good data is well described data
- 10. • Representation of important things in a specific domain • Describes types of entities (e.g. cells) and relations between them • An active, formal computational artifact • A mathematical model based on a subset of first order logic • Tools can automatically process ontologies for analysis - e.g. gene expression enrichment analysis • A communication tool • Provides a dictionary for collaborators, a shared understanding • Allows data sharing Use Ontologies Myeloid Leucocyte Monocyte CD14+ Monocyte
- 11. • OLS - The Ontology Lookup Service • http://www.ebi.ac.uk/ols/index • Indexes 150 biomedical ontologies • (4.5 million terms, 11 million relations) • Zooma • http://www.ebi.ac.uk/spot/zooma/ • Using past knowledge to inform new annotation • Curated mappings from the Expression Atlas, Open Targets and others • Webulous • http://www.ebi.ac.uk/spot/webulous/ • GoogleSheets template system • Create new ontology terms • OXO (in beta) • http://www.ebi.ac.uk/spot/oxo/ • Cross references between ontologies • All services have API and UI access Webulous Use Ontologies
- 12. Supporting deposition of well described data FAANG Validation Service Validates completed metadata Excel templates and prepares metadata for archive submission http://www.ebi.ac.uk/vg/faang
- 13. Supporting deposition of well described data •Checks ontologies (scope, accuracy, terms). •Relationships (familial, breeds). •Minimum standards and validity.
- 14. Supporting deposition of well described data
- 15. Supporting deposition of well described data
- 16. Supporting deposition of well described data •On conversion, validates again and checks project information. •If passes, returns correctly formatted SampleTab for BioSamples and XML for ENA.
- 17. Supporting deposition of well described data • The Validation service code and website • http://www.ebi.ac.uk/vg/faang •https://github.com/faang/faang-metadata • https://github.com/FAANG/validate-metadata
- 18. How much data? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Donor Specimen Gallus Gallus Ovis aries Sus scrofa Bos taurus Bubalus bubalis Capra hircus 1240 8428 Specimens 285 Donor Animals 132 62 56 14 13 8 678 2479 1423 1667 941
- 19. How much data? 132 62 56 14 13 8 678 2479 1423 1667 941 8 European Nucleotide Archive studies submitted • 4891 sequencing runs Largest submission • RNA sequencing of tissues and cell types from Scottish Blackface x Texel sheep for transcriptome annotation and expression analysis, The Roslin Institute • 3994 sequencing runs
- 20. Finding the FAANG Data http://data.faang.org/home
- 21. Finding the FAANG Data http://data.faang.org/organism
- 22. Finding the FAANG Data http://data.faang.org/organism/SAMEA103886117
- 23. Finding the FAANG Data http://data.faang.org/specimen/SAMEA103886170
- 24. Finding the FAANG Data •More Data • Additional FAANG data • Other livestock data using legacy standards •Standard Analysis products •Trackhub links •Better search •Sortable tables
- 25. Who is helping you? Peter Harrison Jun Fan faang-dcc@ebi.ac.uk
- 26. Overview 0% 20% 40% 60% 80% 100% Donor Specimen
- 27. Questions? Find out how to submit data http://bit.ly/FAANGArchiveGuide Ask for help faang-dcc@ebi.ac.uk @faangomics on twitter Let us know about your project http://bit.ly/FAANGProjectRegistry
Editor's Notes
- I have a good example from Tara Oceans of where metadata relating to samples allows image and sequence samples to be aligned and a close ecological relationship to be discovered between an alga and a diatom - essentially, hight-throughput sequence data showed more-than-expected co-location of two species, this led to paring down to a number of bodies of water in given locations (metadata), high-throughput image samples from the same bodies of water could then be selectively inspected to reveal how close the ecological relationship was.
- I have a good example from Tara Oceans of where metadata relating to samples allows image and sequence samples to be aligned and a close ecological relationship to be discovered between an alga and a diatom - essentially, hight-throughput sequence data showed more-than-expected co-location of two species, this led to paring down to a number of bodies of water in given locations (metadata), high-throughput image samples from the same bodies of water could then be selectively inspected to reveal how close the ecological relationship was.
- Why Improve your analysis Easier to find batch effects and confounding factors Make your data usable Reduce ambiguity Facilitate reproduction of results Improve integration across labs, projects and data modalities Make your data discoverable Other researchers Integration services (Ensembl, Gene Expression Atlas)
- Why Improve your analysis Easier to find batch effects and confounding factors Make your data usable Reduce ambiguity Facilitate reproduction of results Improve integration across labs, projects and data modalities Make your data discoverable Other researchers Integration services (Ensembl, Gene Expression Atlas)