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BioRDF Breakout

BioRDF Breakout. Introduction – Kei Cheung Mage-tab – Michael Miller vOID – Jun Zhao (remote) aTag – Matthias Samwald (remote) Discussion – All. BioRDF Breakout: Microarray Use Case. Kei Cheung, Ph.D. Associate Professor Yale Center for Medical Informatics.

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BioRDF Breakout

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  1. BioRDF Breakout • Introduction – Kei Cheung • Mage-tab – Michael Miller • vOID – Jun Zhao (remote) • aTag – Matthias Samwald (remote) • Discussion – All

  2. BioRDF Breakout: Microarray Use Case Kei Cheung, Ph.D. Associate Professor Yale Center for Medical Informatics HCLS IG Face-to-Face Meeting, Santa Clara, California, November 2-3, 2009

  3. Introduction • Whole-genome expression profiling has created a revolution in the way we study disease and basic biology. • DNA microarrays allow scientists to quantify thousands of genomic features in a single experiment • Since 1997, the number of published results based on an analysis of gene expression microarray data has grown from 30 to over 5,000 publications per year • Major public microarray data repositories have been created in different countries (e.g., NCBI GEO, EBI ArrayExpress, and CIBEX)

  4. Microarray Workflow

  5. An Example of differentially expressed genes

  6. Importance of Integrating Microarray Data • Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, • Very few common identified marker genes among different studies involving patients with the same disease. • It is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. • The integration of external information resources is essential in interpreting intrinsic patterns and relationships in large-scale gene expression data

  7. Microarray Data Standards • MGED • MIAME • MAGE-ML • MAGE-TAB

  8. Some Examples • Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes (Jiang et al. 2004 BMC Bioinformatics) • Large-scale integration of cancer microarray data identifies a robust common cancer signature (Xu et al. 2007 BMC Bioinformatics) • What about neurosciences?

  9. Access to and Use of Microarray data in Neuroscience • NIH Neuroscience Microarray Consortium • Public repositories such as GEO and ArrayExpress (including data generated from neuroscience microarray experiments) • Brain atlases (e.g., Allen Brain Atlas and GenSAT)

  10. Neuron ontology Brain region (e.g., entorhinal cortex, hippocampus, primary visual cortex) Part-of Input to Layer (e.g., Layer 2 of the enthorhinal cortex) Part-of Neuron (e.g., stellate island neuron, pyramidal neuron) Ontology-Based Integration Microarray experiment 1 Microarray experiment 2

  11. Example Federated Queries • Retrieve a list of differentially expressed genes between different brain regions (e.g., hippocampus and entorhinal cortex) for normally aged human subjects. • Retrieve a list of differentially expressed genes for the same brain region of normal human subjects and AD patients. • Using these lists of genes one can issue (federated) queries to retrieve additional information about the genes for various types of analyses (e.g., GO term enrichment).

  12. Microarray Experiment Descriptions E-GEOD-3296 Transcription profiling of primary mouse embryonic fibroblasts (MEFs) from C57B1/6x129/Sv F2 e14.5 embryos that contain a deletion in the CH1 domain of three of four alleles of CBP and p300 The CH1 protein interaction domain of the transcriptional coactivators p300 and CBP is thought to interact with HIF-1alpha and this interaction is thought to be critical to the expression of HIF-1alpha target genes in response to hypoxia. Trichostatin A (TSA), an inhibitor of histone deacetylases, has been reported to repress the expression of HIF-1alpha target genes. To test the requirement of the CH1 domain and TSA for gene expression in response to dipyridyl (a hypoxia mimetic), primary mouse embryonic fibroblasts (MEFs) were generated from C57Bl/6x129/Sv F2 e14.5 embryos that contain a deletion in the CH1 domain of three of four alleles of CBP and p300. The remaining allele of p300 or CBP was a conditional knock out allele. Control MEFs with only a single conditional knockout allele of p300 or CBP were also generated. At passage 3 MEFs were infected with Cre Adenovirus and grown until they had expanded at least 100 fold. Subconfluent MEFs were treated with ethanol vehicle or 100ng/ml TSA with 5% carbon dioxide at 37 C in a humid chamber for 30 min., followed by ethanol vehicle or 100 umdipyridyl (DP) for an additional 3hrs. Immediately after treatment, cells were lysed in Trizol for RNA extraction. E-GEOD-3327 Transcription profiling of different regions of mouse brain to study adult mouse gene expression patterns in common strains. Adult mouse gene expression patterns in common strains. Experiment Overall Design: six mouse strains and seven brain regions were analyzed E-GEOD-358 Transcription profiling of rat whole brain samples from animals with repeated exposure to the anaesthetic isoflurane 12 Controls, 3 5-exposures, 3 10-exposures. Rats were exposed to 90 minutes of 1.0% isoflurane twice a day for a total of 5 or 10 exposures. Animals did not require intubation. All exposures and hybridizations were performed at the Univ. of Pennsylvania

  13. Open Biomedical Annotator

  14. Some Results • Two microarray experiments (E-GEOD-4034, E-GEOD-4035) contain the following set of terms: fear, hippocampus, mouse. • These microarray experiments study the role of hippocampus in fear using mouse as the model.

  15. Analysis tools • BioConductor • GenePattern • Genespring

  16. Intercommunity collaboration HCLS (BioRDF) MGED (ArrayExpress) NIF (NeuroLex) Ontology community (NCBO)

  17. cel, gpr, etc Web of silos

  18. Semantic Web = Brilliant Web!

  19. The End

  20. Discussion • What is the RDF structure • Extension of SPARQL to empower data analysis • Workflow and provenance • Visualization • How to integrate database and literature • Integration of other types of data • Inter-community collaboration • Translational use cases

  21. What should be the RDF structure? • Experiments • Samples • Experimental conditions/factors • Gene lists • Arrays/chips • Raw/processed data (e.g., CEL, GPR, gene matrix)

  22. Extension of SPARQL • Hierarchical queries • Statistical analyses/tests • Enrichment analysis

  23. Workflow and provenance • Taverna • Biomoby • Genepattern

  24. Visualization • Cytoscape • TreeView

  25. How to integrate database and literature

  26. Inter-community Collaboration • NCBO • SWAN

  27. What other types of data can be integrated with microarray data

  28. Translational use cases

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