1 / 26

Integrating Genomic Databases

Integrating Genomic Databases. Chris Stoeckert, Ph.D. Computational Biology and Informatics Laboratory. Talk Outline. Challenge of integrating biological data Federations vs warehouses GUS/RAD - warehouse approach K2 - connecting to other systems. Challenge of Integrating Biological Data.

yrowen
Télécharger la présentation

Integrating Genomic Databases

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Integrating Genomic Databases Chris Stoeckert, Ph.D. Computational Biology and Informatics Laboratory

  2. Talk Outline • Challenge of integrating biological data • Federations vs warehouses • GUS/RAD - warehouse approach • K2 - connecting to other systems

  3. Challenge of Integrating Biological Data • Many sources of different types • Different types of data • Biological sequence (DNA, RNA, protein) • Gene expression • Structure • Etc… • Different representations of data • Flat file • Relational • Object-oriented • Etc… • Imposing semantics of biology • Genes and RNAs and Proteins are related • But may have different names • Biology is context dependent

  4. Disease Treatment Devel. Stage Anatomy Sample Label ExperimentSample Taxon Exp.ControlGenes Hybridization Conditions Experiment ControlGenes Groups ExpGroups Examples of Different Sources and Types

  5. Different Technologies for the Same Data Type

  6. Why Bother to Integrate? Remember the fable of the blind men and the elephant! http://www.noogenesis.com/pineapple/blind_men_elephant.html

  7. Federations vs Warehouses • Federations • Link to everybody • Always current • Generally stuck with data as is • Warehouses • Bring everything in house • Can cleanse and add value to integrated data • Staying up to date Davidson et al. IBM Systems Journal 2001

  8. View and Warehouse Integration

  9. GUS/RAD - Warehouse Approach • Gene Discovery • EST analysis • Genomic sequence analysis • Gene Regulation • Microarray analysis • Promoter/ regulatory region analysis • Biological data representation • Data integration • Ontology

  10. Computational Biology and Informatics LaboratoryOctober, 2001

  11. Controlled vocabs. free text • GO • Species • Tissue • Dev. Stage under development GUS: Genomics Unified Schema • Genes, gene models • STSs, repeats, etc • Cross-species analysis Genomic Sequence RAD RNA Abundance DB • Characterize transcripts • RH mapping • Library analysis • Cross-species analysis • DOTS Transcribed Sequence Special Features • Arrays • SAGE • Conditions Transcript Expression • Ownership • Protection • Algorithm • Evidence • Similarity • Versioning • Domains • Function • Structure • Cross-species analysis Protein Sequence Pathways Networks • Representation • Reconstruction

  12. Experiment Raw Data Platform Metadata Processed Data Algorithm RAD: RNA Abundance Database Compliant with the MGED standards

  13. Clusters vs. Contig Assemblies UniGene Transcribed Sequences (DOTS) CAP4: (Paracel) -Consensus Sequences -Alternative splicing -Paralogs BLAST: Clusters of ESTs & mRNAs

  14. Bridging Fingerprint Contigs and RH Maps on Mouse Chromosome 5 Crabtree et al. Genome Research 2001 Fingerprint Map Chr. 5 RH Map

  15. Identify shared TF binding sites Genomic alignment and comparative Sequence analysis TESS (Transcription Element Search Software) PROM-REC (Promoter recognition) RAD GUS EST clustering and assembly

  16. Assembled Transcripts About 3 million human EST and mRNA sequences used Combined into 797,028assemblies Cluster into 150,006 “genes” Can identify a protein for 76,771 genes And predict a function for 24,127 genes About 2 million mouse EST and mRNA sequences used Combined into 355,770 assemblies Cluster into 74,024 “genes” Can identify a protein for 34,008 genes And predict a function for 15,403 genes

  17. CBIL Project Architecture Sequence & annotation Gene index (ESTs and mRNAs) Microarray expression data experimental annotation Relational DB (Oracle) with Perl object layer GUS RAD

  18. AllGenes

  19. AllGenes Enhancements: Genomic Data

  20. http://plasmodb.org

  21. EPConDB Pathway query

  22. View and Warehouse Integration

  23. K2 - connecting to other systems

  24. Linking GUS to Other Sources GUS Neurocartographer K2 Medline What papers have been published on genes that are expressed in this part of the brain?

  25. CBIL: Chris Stoeckert Vladimir Babenko Brian Brunk Jonathan Crabtree Sharon Diskin Greg Grant Yuri Kondrakhin Georgi Kostov Phil Le Li Li Junmin Liu Elisabetta Manduchi Joan Mazzarelli Shannon McWeeney Debbie Pinney Angel Pizarro Jonathan Schug PlasmoDB collaborators: David Roos Martin Fraunholz Jesse Kissinger Jules Milgram Ross Koppel, Monash U. Malarial Genome Sequencing Consortium (Sanger Centre, Stanford U., TIGR/NMRC) EPConDB collaborators: Klaus Kaestner Marie Scearce Doug Melton, Harvard Alan Permutt, Wash. U Comparative Sequence Analysis Collaborators: Maja Bucan Shaying Zhao Whitehead/MIT Center for Genome Research K2/DARPA: Sue Davidson Scott Harker Jonathan Nissanov Carl Gustafson Acknowledgements http://www.cbil.upenn.edu

More Related