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
Imposing semantics of biology
Genes and RNAs and Proteins are related
But may have different names
Biology is context dependent

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

5. Different Technologies for the Same Data Type

6. Why Bother to Integrate?
Remember the fable of the
blind men and the elephant!

7. Federations vs Warehouses
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 Laboratory October, 2001

11. GUS: Genomics Unified Schema
free
text
Controlled vocabs. GO
Species
Tissue
Dev. Stage
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
Transcript
Expression
Arrays
SAGE
Conditions
Ownership
Protection
Algorithm
Evidence
Similarity
Versioning
under development
Domains
Function
Structure
Cross-species analysis
Protein
Sequence
Pathways
Networks
Representation
Reconstruction

12. RAD: RNA Abundance Database
Experiment
Platform
Raw Data
Processed
Data
Algorithm
Metadata
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. Crabtree et al. Genome Research 2001
Bridging Fingerprint Contigs and RH Maps on Mouse Chromosome 5
Crabtree et al. Genome Research 2001
Fingerprint Map
Chr. 5 RH Map

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

16. Assembled Transcripts
About 3 million human EST and mRNA sequences used
Combined into 797,028 assemblies
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.
New site

22. EPConDB Pathway query

23. View and Warehouse Integration

24. K2 - connecting to other systems

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

26. Acknowledgements http://www.cbil.upenn.edu 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