![DOI citations within DASs
Most popular data repositories based on DOI citations in DASs (Jan-Mar 2019)
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GWAS and DAS
- 1. Jo McEntyre, EMBL-EBI Mining Data Availability Statements for GWAS data
- 2. GWAS and the GWAS Catalog • GWAS analyse variants across the genome to identify loci associated with a disease or phenotype Study metadata including: - Trait - Sample information Publication information Results - Lead associations - Summary statistics GWAS Catalog data
- 3. GWAS Catalog content As of October 2019 • 4,220 publications • 7,661 studies • 157,336 variant-trait assoc. • 276 pubs with summary statistics, >8,000 datasets www.ebi.ac.uk/gwas
- 4. What is Europe PMC? Europe PMC– free digital archive of biomedical and life sciences research publications
- 5. Content in Europe PMC Europe PMC is a partner in PubMed Central International
- 6. Text mining infrastructure • Gene-disease relationships • Mutations • GeneRIFs • Diseases and phenotypes • Phosphorylation events • Transcription factor-target interactions • Organisms • Gene/proteins • GO terms • ChEBI • EFO • Grants • Accession numbers
- 7. Text mining platform: SciLite application
- 8. Accession numbers mined from full text publications ELIXIR Core Data Resources and Deposition Databases
- 9. Cross-links between GWAS and Europe PMC
- 10. Data Availability statements in Europe PMC
- 11. <title> and XML path Title XML path Frequency Data Availability article:front:notes 90,928 Data accessibility article:back:sec 2,694 Data Availability article:back:sec:fn-group 2,580 Data article:body:sec 2,265 Availability of supporting data article:body:sec 1,593 Major datasets article:back:sec:sec 1,074 Database survey article:body:sec 986 Extended Data article:body:sec 851 Data availability article:body:sec 795 Extended Data Figure 1 article:body:sec:SecTag:fig 689 Top 10 combinations of <title> content containing “data” and XML path
- 14. Curating papers for the GWAS catalog
- 15. GWAS Catalog literature identification: Query based vs machine learning Query-based Machine learning Precision 6% 27% Recall 100% 96% Improved efficiency 80% reduction in publications to review average 144 to 30/week
- 16. Summary statistics in the GWAS Catalog by publication year % of publications with summary statistics over time & in the whole Catalog
- 17. Summary statistics for users Facilitating data integration and downstream analyses
- 19. The end
- 20. GWAS Catalog literature identification • Previously used manual query based search term • Query: genomewide OR genome wide OR genome-wide OR GWAS • Now replaced with machine learning based search • convolutional neural net trained on corpus of GWAS Catalog publications • Collaboration with Zhiyong Lu’s group Lee et al, PMID 30102703 , PloS Comp Bio • ML results triaged by curator in custom Pubtator interface
- 21. Old literature search and triage process • Manual search in PubMed • Query: genomewide OR genome wide OR genome- wide OR GWAS • Curator assesses each publication for eligibility for inclusion in GWAS Catalog • Specific eligibility criteria https://www.ebi.ac.uk/gwas/docs/methods/criteria • Genome wide association study of >100,000 variants distributed genome
- 22. Deep learning algorithm (convolutional neural net) trained on corpus of GWAS Catalog publications) Figure 1. Lee et al, PMID 30102703 , PloS Comp Bio Machine learning search Corpus of GWAS Catalog publications
- 23. GWAS Catalog machine learning literature search method • Precision 27% • Recall 96% Table 3. Lee et al, PMID 30102703 , PloS Comp Bio
- 24. Machine learning: • Improved efficiency (80% reduction in publications to review, 144 to 30/week) • Similar capture of eligible studies GWAS Catalog machine learning literature search method vs query based search Table 3. Lee et al, PMID 30102703 , PloS Comp Bio
- 25. Uses Narrow-down/prioritise candidate loci Drug target discovery Predict disease risk Understand disease mechanism Statistics on disease data and research
- 26. DOI citations within DASs Most popular data repositories based on DOI citations in DASs (Jan-Mar 2019) (?i)(10[.]d{4,9})(?=/)(?=[-._;()/:A-Z0-9]+)