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Session V: Life Science Identifiers - Use Cases, Future Directions

Session V: Life Science Identifiers - Use Cases, Future Directions. Recent History. LSIDs 3 years old I3C evaluating AGAVE, BSML encoded IDs as tuples/triples If we could not agree on a data standard, could we at least agree on how we write the identifiers. Today. OMG Spec

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Session V: Life Science Identifiers - Use Cases, Future Directions

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  1. Session V: Life Science Identifiers - Use Cases, Future Directions

  2. Recent History • LSIDs 3 years old • I3C evaluating AGAVE, BSML • encoded IDs as tuples/triples • If we could not agree on a data standard, could we at least agree on how we write the identifiers

  3. Today • OMG Spec • google “+LSID +bioinformatics” • 686 results (10/27/04, 2:40pm) • 700 results (10/27/04, 7:20am)

  4. Broad Use Cases

  5. How GenePattern is using LSIDs • Identify analysis tasks and pipelines via LSIDs • Create sharable pipelines referencing tasks via LSIDs • Provide a repository and retrieval for analysis tasks by LSID

  6. Example: ALL/AML Analysis Training Data Test Data all_aml_train 27 ALL, 11 AML expression samples all_aml_test 20 ALL, 14 AML expression samples Preprocess Filter uninformative genes Preprocess Filter uninformative genes SOM Clustering Cluster samples to separate tumor types Weighted Voting Train-test Build a classifier and compute its accuracy on a test set Class Neighbors Find genes that most closely match a profile Weighted Voting Cross-Validation Build a classifier and compute its accuracy using cross-validation Golub and Slonim et al., 1999

  7. Example: ALL/AML Analysis urn:lsid:broad.mit.edu:cancer.software.genepattern.module.pipeline:00001:0 Training Data Test Data all_aml_train 27 ALL, 11 AML expression samples all_aml_test 20 ALL, 14 AML expression samples Preprocess urn:lsid:broad.mit.edu :cancer.software.genepattern.module.analysis:00020:0 Preprocess urn:lsid:broad.mit.edu :cancer.software.genepattern.module.analysis:00020:0 SOM Clustering urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00029:0 Weighted Voting Train-test urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00027:0 Class Neighbors urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00001:0 Weighted Voting Cross-Validation urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00028:0 Golub and Slonim et al., 1999

  8. LSIDs enable • Reproducible research • exactly repeating an in silico experiment • ‘modernizing’ pipelines to latest • Tracking module provenance • Someday • Data will be available via LSID too…

  9. Future… urn:lsid:broad.mit.edu:cancer.software.genepattern.module.pipeline:00001:0 Training Data Test Data urn:lsid:broad.mit.edu: cancer.microarray: abcde:1.0 urn:lsid:broad.mit.edu: cancer.microarray: zyxwv:1.0 Preprocess urn:lsid:broad.mit.edu :cancer.software.genepattern.module.analysis:00020:0 Preprocess urn:lsid:broad.mit.edu :cancer.software.genepattern.module.analysis:00020:0 SOM Clustering urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00029:0 Weighted Voting Train-test urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00027:0 Class Neighbors urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00001:0 Weighted Voting Cross-Validation urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00028:0 Golub and Slonim et al., 1999

  10. Other LSID use at the Broad • Sample management • Sharing samples (tissues, clones, etc) between program groups • LSIDs identify samples • Permits scientists to find all experiments done with a sample in any Broad program

  11. Other LSID use at the Broad 2. GeneCruiser web service • annotation web service for microarray probes • maps probe set identifiers to GO, GenBank, SwissProt etc • Interface returns LSIDs to these other sources for their identifiers

  12. Use Cases and Future Directions • What does it actually mean to identify a biological object such as "a gene"? • How does LSID address structural elements of biological and chemical objects? • What are the lessons learned from early implementations of LSID?

  13. Use Cases and Future Directions • What granularity of object do we identify? • Should LSID be a URI not a URN? • Should virtual persistent identifiers for derived/calculated properties be used? • What are the barriers to widespread use? • Data/Metadata split – is this a problem? • Phil Lord mentioned @end of yesterday in MyGrid talk

  14. Best LSID quote… • “LSIDs are in a sense just a sociological con trick, since they are nothing more than cheap and cheerful URNs” –David Shotten

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