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National Center for Microscopy and Imaging Research Center for Research in Biological Systems PowerPoint Presentation
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National Center for Microscopy and Imaging Research Center for Research in Biological Systems

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  1. Tools for Federation of Brain Data: The Cell Centered Database and BIRN projects Maryann E. Martone, Ph. D. National Center for Microscopy and Imaging Research Center for Research in Biological Systems University of California, San Diego

  2. Increasing Access to Data The Cell Centered Database Make 3D microscopic data available to the scientific community • High resolution light and electron microscopy • Link data obtained at cellular and subcellular scale to molecular and higher order structure • Most of our data is neural but data model is generic • Built on the BIRN infrastructure http://ccdb.ucsd.edu

  3. Research prototype for imaging information Advanced features for representation and querying content of cell level data Venue for sharing cell level 3D microscopic data Data sets from tomography and correlated microscopy are made freely available to the scientific community Imaging management system for microscopy data Detailed data model for tracking and managing imaging data Part of BIRN database federation NCMIR’s contribution to Mouse BIRN The CCDB

  4. tilt mosaic LM time EM serial survey Project • Over 80 tables • Public, semi-public and private security levels Experiment Overview of CCDB Schema Subject Methodological characterization Tissue Product Tissue Processing Fixation Protein Localization Staining Embedding Anatomical Details Microscopy Product Microscopy Image Details Region of Interest Reconstruction Image Details Reconstruction Segmentation Tree Tracing Morphometrics

  5. Simple Web Query Interface

  6. Interactive Data Forum

  7. Smart Atlas: A GIS-based system for multiscale integration and query. Haiyun He, Joshua Tran, Ilya Zaslavsky

  8. Where is the “Cell” in the CCDB? • CCDB is a “primary data” base not a “knowledge” base • Data model was elaborated around the techniques used to acquire images • Correlations between subcellular and cellular structure • Current view on web page is administrative/methodological • “Cell Centered Views” are being constructed through ontologies, the creation of cell-level atlases for classes of neurons and the creation of new data types for cell morphology

  9. Data Types for Neuronal Data Shenglan Zhang, Yujun Wang, Amarnath Gupta

  10. Calsequestrin in Cerebellum

  11. Ontology for Subcellular Anatomy of Nervous System

  12. National Center for Research Resources • Establish cyberinfrastructure for storing, manipulating and sharing data and resources • High speed, robust connectivity via Internet2 • Data and computational resources • Current test beds focused on neuroimaging • Human MRI • Human fMRI • Mouse models of neurological disease • *Will no longer matter where data, instruments and computational resources are located physically” BIRNBiomedical Informatics Research Network

  13. BIRN Virtual Data Grid

  14. Duke Database Mediation for BIRN Find animal models of movement disorders where the volume of basal ganglia structures are decreased and where loss of spines from medium spiny neurons is observed. Integrated View Knowledge Sources Integrated View Definition Mediator Wrapper Wrapper Wrapper Wrapper UCSD UCLA Cal Tech

  15. CCDB Dictionary

  16. Mouse BIRN Data Federation Yujun Wang UCSD

  17. Mediator wrapper wrapper wrapper wrapper Duke UCLA Cal Tech The Smart Atlas: A Grid-based GIS tool for spatial integration of multiscale distributed brain data Ilya Zaslavsky, Joshua Tran, Haiyun He, Amarnath Gupta SRB UCSD

  18. Know Me Ontology Browser Smart Atlas Integration Engines Spatial and Semantic Mark up Data BAMS Knowledge Web Forms Genomic Data

  19. Planned Enhancements • Populate the database • Electronic lab notebooks --> CCDB using XML • Implement additional annotation capabilities • Implement new data types • Create advanced queries based on new data types and ontologies • Develop APIs and other aids to interoperability • Facilitate use of data in CCDB to aid in model construction (XML output) • Revision of CCDB schema • Continued Research: • Cell atlases • Ontologies for subcellular data

  20. Querying Spatially Distributed Signals Spatial Histogram: Yujun Wang, Simone Santini, Amarnath Gupta