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Software servers search tool versioning programming XML RDF OWL data sets semantic web PubChem screening fluorescence small molecule biological assay novel.

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Presentation on theme: "Software servers search tool versioning programming XML RDF OWL data sets semantic web PubChem screening fluorescence small molecule biological assay novel."— Presentation transcript:

1 software servers search tool versioning programming XML RDF OWL data sets semantic web PubChem screening fluorescence small molecule biological assay novel chemical tools chemical probes high-thoughput screening (HTS) ChemBank PDSP pharmaceutical chemical biology cheminformatics biological pathways disease networks structural biology biomedical knowledge technologyend point ATP Luciferin Coupled activity viability Beta-Lactamase Induction binding based calcium redistribution caspase activity dehydrogenase activity cyclic AMP redistribution energy transfer enzyme reporter enzyme substrate based Fluorogenic substrate GFP induction standards controlled vocabulary indexing subject indexing schemes authorized terms taxonomies thesauri subject headings natural language library tags homographs synonyms polysemes concepts structure search knowledge specificity article meta-data information exchange classification nomenclature semantic domain properties annotation object classes individuals BioAssay Ontology (BAO) Stephan Schürer, PhD ICBO, Buffalo, July 30 2011 sschurer@med.miami.edu

2  One of the most important approaches to find novel entry points for drug discovery programs  Historically in pharmaceutical companies  Since ~2005, massive NIH effort (MLI) to make HTS accessible to public sector research  PubChem is the major repository of HTS data  More recently: EU-OpenScreen project Background for BioAssay Ontology High-throughput screening 2

3  Lack of standardized assay annotations  No standardized endpoint names or formats  Data is rarely re-used(!)  Common queries cannot be asked  Analysis across different data sets is difficult  Integration with other databases is difficult  No knowledge model for assays and screening results Motivation for BioAssay Ontology Large public screening data sets PubChem, ChEMBL, PDSP, ChemBank, Binding DB 3

4 Identify inhibitors of kinases in biochemical assays. Identify compounds active in multiple luciferase reporter gene assays. Identify compounds active in cell viability assays and organize by cell lines and assay types. Identify active compounds in assays related to pathway X. … Queries the Ontology should be able to answer 4

5 5  Leverage the aggregated corpus of publically available HTS data to infer molecular mechanism of actions (MMOA) of small molecule perturbagens in biological model systems. Schürer et al. “BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-throughput Screening Data Sets” J Biomol Screen 2011 (16), 415-426.

6  BAOSearch Software (beta): http://baosearch.ccs.miami.edu http://baosearch.ccs.miami.edu  Query, explore, download BAO-annotated PubChem content  Some semantic search capabilities  Project Website and Wiki with relevant materials and documentation: http://www.bioassayontology.org/ http://www.bioassayontology.org/wiki http://www.bioassayontology.org/ http://www.bioassayontology.org/wiki BAO Products and Resources 6

7  Application / user focus vs. “universal” ontologies  Efficiency vs. “realism” of representations  Rapid application development  Orthogonal ontologies vs. Ontology mapping  Universal “realism” vs. domain or application-specific  Chemical bond: 2D structure graph, 3D rule based, molecular mechanics, semi-empirical, up-initio QM  Disease  Virtual world Questions / Discussion points 7

8  Collaborative ontology development  Collaborative vs. individual effort  Control over development and focus / application focus  Rapid application development  Quality  Aligning BAO to upper level ontology (BFO)  Benefits vs. required resources  Do upper level ontologies matter for specialized applications? Questions / Discussion points 8

9  Aligning BAO with OBI  Some level of overlap  OBI: process-oriented (model the investigation)  BAO: purpose of categorization and analysis of HTS data  BAO model becomes more complex if based on OBI  How do we do it practically  Define missing assays to OBI and MIREOT back?  Quick term templates (QTT)?  Define our relations as short-cut relationships (using RO)? Questions / Discussion points 9

10 Additional slides 10

11 BAO-facilitated Example for Analysis (Luciferase Assays) Details in: Schürer et al. “BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-throughput Screening Data Sets” J Biomol Screen 2011 (16), 415-426. 11

12 Panel Assay Single Conc Other Conc-response Assay Count Most promiscuous reporter gene compounds

13 Reporter DR Reporter SC Viability DR Viability SC Enz Activ DR Enz Activ SC ATP DR ATP SC Luciferin DR Luciferin SC Promiscuity Index 0 1 0.2 Compounds Luciferase Enzyme Inhibitors Generally cytotoxic

14 Examples: Cytotoxic Series Cluster Reporter PCIdx: 0.56 Cluster Reporter Active: 58 Cluster Viability PCIdx: 0.64 Cluster Viability Active 27 Cluster Reporter PCIdx: 0.48 Cluster Reporter Active: 23 Cluster Viability PCIdx: 0.45 Cluster Viability Active 10 Cluster Reporter PCIdx: 0.41 Cluster Reporter Active: 29 Cluster Viability PCIdx: 0.57 Cluster Viability Active 13 Daunorubicin Emetine

15 Examples: Luciferase Inhibitor Series Cluster Size: 6 Cluster Reporter PCIdx: 0.61 Cluster Reporter Active: 101 Cluster EnzActivity PCIdx: 0.58 Cluster EnzActivity: 15 Cluster Size: 4 Cluster Reporter PCIdx: 0.38 Cluster Reporter Active: 52 Cluster EnzActivity PCIdx: 0.61 Cluster EnzActivity: 11 Cluster Size: 5 Cluster Reporter PCIdx: 0.46 Cluster Reporter Active: 77 Cluster EnzActivity PCIdx: 0.58 Cluster EnzActivity: 14 Schürer et al. “BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-throughput Screening Data Sets” J Biomol Screen 2011 (16), 415-426.

16 1)Development of the Bioassay Ontology 2)Annotation of assays and assay results (content curation) 3)Development of software tools BAO Project: Three major components 16

17 BAO design to describe assays

18 Application of BAO: BAO Search Software 18

19 19 http://baosearch.ccs.miami.edu/baosearch/

20 20 BAO: Concept Search

21 21 Biochemical Assays with IC50 < 1  M

22 22

23 23 Chemical structure search

24  BioAssay Ontology (NCBO bioportal and project site): http://bioportal.bioontology.org/ontologies/45410 http://www.bioassayontology.org/visualize/ http://bioportal.bioontology.org/ontologies/45410 http://www.bioassayontology.org/visualize/  Terminology / annotations for biochemical assays: http://www.bioassayontology.org/ > Assay Annotation Template http://www.bioassayontology.org/  Over 1000 BAO-annotated assays from PubChem (available in BAOSearch) BAO Products and Resources 24

25 Chris Mader Amar Koleti Nakul Datar Sreeharsha Venkatapuram Felimon Gayanilo Mark Southern Saminda Abeyruwan Uma Vempati Magdalena Przydzial Kunie Sakurai Robin Smith Yuanyuan Jia Caty Chung Ubbo Visser Vance Lemmon Mitsunori Ogihara Nick Tsinoremas http://bioassayontology.orgsschurer@med.miami.edu Acknowledgements 25


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