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CCS Staff Meeting

CCS Staff Meeting. February 11 th , 2010 Robin Smith. Experimenting in parallel. 24 wells. 12 wells. 6 wells. 96-, 384- and 1536-well plates. http://thumbs.dreamstime.com. http://www.genome.gov/. High throughput. http://www.piramallifesciences.com/. Lemmon Lab: Medium Throughput.

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CCS Staff Meeting

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  1. CCS Staff Meeting February 11th, 2010 Robin Smith

  2. Experimenting in parallel 24 wells 12 wells 6 wells 96-, 384- and 1536-well plates http://thumbs.dreamstime.com http://www.genome.gov/

  3. High throughput http://www.piramallifesciences.com/

  4. Lemmon Lab: Medium Throughput

  5. Evolution of “drug discovery” ~2000’s Systems chemical biology / network (poly)-pharmacology, chemically tractable target combinations, phenotypic screening 1980’s Validated biological targets; selective modulators (molecular biology, HTS, combinatorial chemistry etc.) Synthetic compounds’ phenotypic effect (animal testing) 1950’s Identification and first synthesis of active (natural) components ~1900’s ~800BC Natural extracts’ observed effect (traditional human experience)

  6. High Throughput Screening Campaigns

  7. What does the data look like?

  8. Where does all of that data go? • Within Pharmaceutical Companies: • Internal data warehouses (relational databases) • Other private informatics companies that specialize in analysis, e.g. GeneGo, NextBio, CDD • Within Universities • Excel spreadsheets • University IT provided storage systems • Public data stores, e.g. ChEMBL, PDSP, PubChem

  9. PubChem: repository for HTS data

  10. Pubchem bioassays by date @Time of grant proposal: 1500 @Time of Robin start: 1885 @Last Month: 2029

  11. The problems with Pubchem • Very generic data repository: Flexible to put data in, difficult to get data out • Few annotations of assay technologies and the underlying biology • No standardization of data • Difficult to search

  12. Example: Pubchem Protocols

  13. Example: Pubchem Endpoints

  14. The BioAssay Ontology Project http://www.bioassayontology.org

  15. BAO Objectives • Develop an ontology in OWL (web ontology language) to describe high-throughput (and high content) bioassays • Annotate data from Pubchem and other sources using BAO concepts and store in a repository • Develop software tools to allow users to browse and visualize annotations in the repository

  16. What can ask with an ontology? • Which compounds affect members of the STAT3 signaling pathway? • Which compounds have similar profiles to my compound but are less toxic? • What cell lines has my kinase been targeted in? • Which compounds are active in a set of related bioassays? • What technologies could be used to test whether my hit compound is acting non-specifically?

  17. Breaking down a BioAssay BioAssay Perturbagen Purpose • e.g.Primary screening, confirmatory • e.g. Counter assay, Alternate Technology e.g. Compound e.g. RNAi Measure Group Endpoint e.g. Activity linked measure For high content assays! Target 3 5 1 2 P 4 Technology e.g. Viability e.g. Binding Format e.g. Primary Cells e.g. Purified Proteins e.g. Activity at 10mM e.g. IC50

  18. Ontology Engineering: Protégé

  19. Text-Mining for Technology Concepts Protocol Enriched Description Enriched

  20. Ontology Visualization

  21. Annotation of assay data

  22. Ontology Team • Stephan Schürer • Vance Lemmon • Ubbo Visser • Robin Smith • Saminda Abeyruwan • Mitsunori Ogihara • Dusica Vidovic • Software Team • Chris Mader • Felimon Gayalino • Nakul Datar

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