![Challenges in propagating knowledge:
Different sources associate phenotypic information with
different aspects of the genotype
fgf8ati282a/ti282a;shhatbx392/+[TL];
cdkn1caMO3-cdkn1ca
Mysm1<tm1a>/Mysm1<tm1a>[C57BL/6]
daf-2(e1370)
ATP1A3(NM_152296.3)
[c.946G>A, p.Gly316Ser]
tin(ABD/346)
Includes gene knockdowns
Includes genetic background
Includes multiple alleles
Single allele w/zygosity
allele
variant/mutation
gene](https://image.slidesharecdn.com/datainteroperabilityhaendelcommonskickoff-171207203618/85/Building-and-traveling-the-data-brick-road-A-report-from-the-front-lines-of-data-integration-22-320.jpg)
![Challenges in propagating knowledge:
Different sources associate phenotypic information with
different aspects of the genotype
fgf8ati282a/ti282a;shhatbx392/+[TL];
cdkn1caMO3-cdkn1ca
Mysm1<tm1a>/Mysm1<tm1a>[C57BL/6]
daf-2(e1370)
ATP1A3(NM_152296.3)
[c.946G>A, p.Gly316Ser]
tin(ABD/346)
Includes gene knockdowns
Includes genetic background
Includes multiple alleles
Single allele w/zygosity
allele
variant/mutation
gene
Transverse spirally arranged
myofibrils are almost
completely absent.
Dystonia 12](https://image.slidesharecdn.com/datainteroperabilityhaendelcommonskickoff-171207203618/85/Building-and-traveling-the-data-brick-road-A-report-from-the-front-lines-of-data-integration-23-320.jpg)
Building (and traveling) the data-brick road: A report from the front lines of data integration
- 1. Building (and traveling) the data-brick road: A report from the front lines of data integration Melissa Haendel, PhD NIH Data Commons Kickoff 2017-12-06
- 3. The data-brick road is a means… …not a destination
- 4. Reuse: the last (and hardest) mileCompliance F A I R VS FAIR FAIR Reuse requires (semantic) integration
- 5. Under- appreciated obstacles to integration? FAIR-TLC “Traceability, Licensing, and Connectedness -- OH MY!” Traceable Licensed Connected
- 6. Traceable Licensed Connected bit.ly/fair-tlc • Data models • Ontologies • Concept alignment • Common Data Elements • Identifiers in service of above Evidence Provenance Attribution • Clearly stated • Comprehensive and non-negotiated • Accessible • Avoid restrictions on kinds of (re)use • Avoid restrictions on who may (re)use Traceability and Licensure go hand-in-hand: Licensing often a (convoluted) credit hack
- 8. A vision for the future of scholarship
- 9. Thomas Markello Dong Chen Justin Y. Kwan Iren Horkayne- Szakaly Alan Morrison Olga Simakova Irina Maric Jay Lozier Andrew R. Cullinane Tatjana Kilo Lynn Meister Kourosh Pakzad Sanjay Chainani Roxanne Fischer Camilo Toro James G. White David Adams Cornelius Boerkoel William A. Gahl Cynthia J. Tifft Meral Gunay-Aygun Hans Goeble Karen Balbach Nadine Pfeifer Sandra Werner Christian Linden Melissa Haendel Peter Robinson Chris Mungall Sebastian Kohler Cindy Smith Nicole Vasilevsky Sandra Dolken Elizabeth Lee Amanda Links Will Bone Murat Sincan Damian Smedley Jules Jacobson Nicole Washington Elise Flynn Sebastian Kohler Orion Buske Marta Girdea Michael Brudno Jeremy Band Melissa Haendel David Adams David Draper Bailey Gallinger Joie Davis Nicole Vasilevsky Heather Trang Rena Godfrey Gretchen Golas Catherine Groden Michele Nehrebecky Ariane Soldatos Elise Valkanas, Colleen Wahl Lynne Wolfe Johannes Grosse Attila Braun David Varga-Szabo Niklas Beyersdorf Boris Schneider Lutz Zeitlmann Petra Hanke Patricia Schropp Silke Mühlstedt Carolin Zorn Michael Huber Carolin Schmittwolf Wolfgang Jagla Philipp Yu Thomas Kerkau Harald Schulze Michael Nehls Bernhard Nieswandt Clinicians/ care team Pathologists Ontologists Informaticians Curators Basic Research The translational workforce: It takes a village to solve disease
- 10. GEO dataset Gemma DRG Genes differentially expressed: ~8,000 gene comparisons Genes significantly expressed or unchanged: ~13,000 gene comparisons Incongruous results Evidence and provenance Importance of raw data alignment (never mind that the gene IDs had to be mapped from strings) Increased: 1,640 Decreased: 1,110 Differential: 2,920 Increased: 4,264 Decreased: 3,833 Differential: 8,133 Both resources recorded 95% confidence intervals for significance
- 11. …how many of these data are truly reusable? Openness is assumed, but …
- 12. Most licenses are vague, non-standard, or missing (n = 51 DBs) Reusabledata.org
- 13. Identifiers are the invisible bedrock of all scientific inquiry; the more complex the question, the greater the reliance on ID hygiene What? Why? How? Identifiers Identifiers & Metadata Identifiers & MetaData & Models Requiredharmonization Question complexity FAIR FAIR How many? FAIR
- 14. Identifier Reality: Not all IDs created equal We need systems that accommodate the heterogeneity Traditional Literature Non- Traditional Persistent Ephemeral Non-existent IdentifierMaturity Scholarly Output Maturity Genomic resources Wild west of identifier tumbleweed
- 15. (way) beyond “linkrot”: Pain points in identifier tech/standards • Versioning and Content evolution AKA “content drift” • Identifier Surrogacy / Granularity • Ambiguous equivalence • Distribution (& replication) of content over multiple providers bit.ly/evidence-of-identifier-pain
- 17. Ambiguous equivalence case 2: Eye of the beholder
- 18. Tangible, actionable community best practice on identifiers for data integration doi:10.1371/journal.pbio.2001414 (bit.ly/id21c-plosbio)
- 19. What integrators are aiming to do is non-trivial
- 20. Ambiguous equivalence case 4: Post-hoc harmonization
- 21. Ambiguous equivalence case 3: Fuzzy Match on xrefs/content How are these 11 records for “Ehlers Danlos Syndrome” related to each other? Narrow synonym? Broad? Exact? Child? Parent? Bayesian models like k-BOOM can help Mungall doi:10.1101/048843 bit.ly/xref-wildwest
- 22. Challenges in propagating knowledge: Different sources associate phenotypic information with different aspects of the genotype fgf8ati282a/ti282a;shhatbx392/+[TL]; cdkn1caMO3-cdkn1ca Mysm1<tm1a>/Mysm1<tm1a>[C57BL/6] daf-2(e1370) ATP1A3(NM_152296.3) [c.946G>A, p.Gly316Ser] tin(ABD/346) Includes gene knockdowns Includes genetic background Includes multiple alleles Single allele w/zygosity allele variant/mutation gene
- 23. Challenges in propagating knowledge: Different sources associate phenotypic information with different aspects of the genotype fgf8ati282a/ti282a;shhatbx392/+[TL]; cdkn1caMO3-cdkn1ca Mysm1<tm1a>/Mysm1<tm1a>[C57BL/6] daf-2(e1370) ATP1A3(NM_152296.3) [c.946G>A, p.Gly316Ser] tin(ABD/346) Includes gene knockdowns Includes genetic background Includes multiple alleles Single allele w/zygosity allele variant/mutation gene Transverse spirally arranged myofibrils are almost completely absent. Dystonia 12
- 24. Decomposition of complex concepts allows interoperability “Palmoplantar hyperkeratosis” increased Stratum corneum layer of skin = Human phenotype PATO Uberon Species neutral ontologies, homologous concepts Autopod keratinization GO “Ulcerated paws” Mouse phenotype =
- 25. Need for a comprehensive and connected picture across resources
- 27. Genes Environment Phenotypes+ = We need interoperability for not only the types of things in our data….
- 28. G-P or D (disease) • causes • contributes to • is risk factor for • protects against • correlates with • is marker for • modulates • involved in • increases susceptibility to G-G (kind of) • regulates • negatively regulates (inhibits) • positively regulates (activates) • directly regulates • interacts with • co-localizes with • co-expressed with P/D - P/D • part of • results in • co-occurs with • correlates with • hallmark of (P->D) E-P • contributes to (E->P) • influences (E->P) • exacerbates (E->P) • manifest in (P->E) G-E (kind of) • expressed in • expressed during • contains • inactivated by …the relationships and their evidence must also be captured
- 29. Data Integrators have deep knowledge to help data providers birth interoperable data
- 30. Thank you Julie McMurry For her illustrative gifts
- 31. Thank you for helping build the data-brick road Melissa Haendel, PhD @ontowonka