1. From EpoDB to EPConDB: Adventures in Gene Expression Databases
Chris Stoeckert, Ph.D.
Computational Biology and Informatics Laboratory

2. EpoDB: A Prototype Database for the Analysis of Genes Expressed During Vertebrate Erythropoiesis
Retrieve all adult, mammalian b-globin gene transcription
units.
Retrieve the proximal promoter regions from all genes with a significant change in expression between the BFU stage and erythroblast stage.
Display the predicted transcription factor binding sites in the
proximal promoter region of the mouse b-globin gene.
Stoeckert, Salas, Brunk, Overton (1999) Nucl. Acids Res. 26:288

3. EpoDB: A Data Warehouse Formed by Information Integration
GENE STRUCTURE:
GenBank
GENE FUNCTION:
Swiss-Prot
TRANSCRIPTION UNIT
(EpoDB)
GENE REGULATION:
Transfac ,TRRD
GENE EXPRESSION:
GERD

4. EpoDB Transcription Unit

5. Populating EpoDB Database: GenBank Swiss-Prot TRRD GERD
Total:
Yes:

6. EpoDB Highlights Reference sequences Controlled vocabularies
“gene ontology”
Specified sequence retrieval
All vertebrates
Controlled vocabularies
Gene names and family names
Experiment descriptions
Gene warehouse
GenBank, SWISS-PROT, Transfac, literature
But no ESTs!

8. EpoDB Gene Landmark Query

9. EpoDB limitations Not scalable
Manual triage of keyword-selected entries
Manual selection of reference genes
Did not handle data from high throughput technologies
No ESTs
Could not represent microarrays, SAGE
Could not make use of unannotated genomic sequence
Difficult to administer
Prolog-based

10. Chronology of CBIL Systems
Gene Expression Sequence Annotation
1995
2001
EpoDB
GAIA
ParaDB
DoTS
Relational DB (Oracle) with Perl object layer
GUS
RAD
EPConDB PlasmoDB AllGenes

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

12. 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

13. GUS Object View Gene Genomic Sequence Gene Instance Gene Feature NA
RNA
RNA
Sequence
RNA
Instance
RNA
Feature
Protein
Protein
Sequence
Protein
Instance
Protein
Feature AA
Sequence AA
Feature

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

15. “Unassembled” clusters (consensus sequences and new)
Incremental Updates of DoTS Sequences
Incoming
Sequences (EST/mRNA)
Make Quality (remove vector, polyA, NNNs)
“Quality” sequences
AssemblySequence
Block with RepeatMasker
Blocked sequences
Assign to DOTS consensus sequences (blastn at 40 bp length, 92% identity)
Cluster incoming sequences that are not covered by consensus sequence.
DOTS Consensus
Sequences
“Unassembled” clusters
Assemble DOTS consensus sequences and incoming sequences with CAP4 - initially reassemble
CAP4 assemblies
(consensus sequences and new)
Calculate new DOTS consensus sequence using weighted consensus sequence(s) and new CAP4 assembly.
New Consensus
sequences
Update GUS database

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. Assembly Validation Alignment to Genomic Sequence via Blast/sim4.
preliminary data look good
Assembly consistency (Assemblies provide potential SNPs)
Add BLAST sim4 figure

18. 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

19. Predicting Gene Ontology Functions

20. RAD Multiple labs Multiple biological systems Multiple platforms
Expressed genes?
Differentially-expressed genes?
Co-regulated genes?
Gene pathways?

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

22. Microarray Gene Expression Database group (MGED)
International effort on microarray data standards:
Develop standards for storing and communicating microarray-based gene expression data
defining the minimal information required to ensure reproducibility and verifiability of results and to facilitate data exchange (MIAME, MAGEML-MAGEDOM)
collecting (and where needed creating) controlled vocabularies/ ontologies.
developing standards for data comparison and normalization.
The schema is compliant with the minimum annotations recommended by MGED.
MIAME: Minimum Information About a Microarray Experiment (common set of concepts that need to be captured in a database to describe gene expression experiments adequately for interpretation, reproduction or critical assessment).
MAML: MicroArray Mark-up Language (XML Document Type Definitions of the concepts).

23. Query RAD by Sample or by Experiment
Access by Experiment
groups
Sample info
ontologies
Image info

25. Different Views of GUS/RAD
Focused annotation of specific organisms and biological systems:
organisms
biological systems
Endocrine
pancreas
Human
Mouse
CNS
GUS
GUS
Plasmodium
falciparum
Hematopoiesis
*not drawn to scale*

26. AllGenes

27. AllGenes “Erythroblast” Query

28. AllGenes Enhancements: Annotated Entries

29. AllGenes Enhancements: Genomic Data

30.
New site

32. Functional Genomics of the Developing Endocrine Pancreas
cDNA libraries from pancreatic tissue
Consortium libraries
Novel genes
relevant dbEST libraries
Microarray studies on pancreatic tissue
Genome wide-survey for genes expressed
Pancreas chip
Validated sequences of interest
Novel sequences from libraries

34. EPConDB: Content and Features
Pancreas clone sets
Panc Chip Clone sets 1.0, 1.5, 2.0
Transcripts found in consortium libraries
Novel transcripts discovered from consortium libraries
Microarray results
Using Incyte’s GEM (genome-wide survey)
Using Panc Chip
Genes expressed in pancreas
AllGenes queries: function, chromosomal location, name, accession
Pathways

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

36. EPConDB Pathway query

37. EPConDB Boolean Query

38. EPConDB History Query

39. EPConDB: Future Developments
Add more microarray results
Provide tools for microarray analysis
Provide genomic alignments
Provide tools for analysis of (putative) promoters

40. Microarray Analysis: Xcluster
Xcluster provided by Gavin Sherlock

41. Microarray Analysis: R statistics
SMA R package from Terry Speed’s group

42. Microarray Analysis: PaGE

43. Future EPConDB Query Result

44. Microarray Analysis: Data download

45. 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

46. Summary EpoDB provides high quality genes for sequence analysis
But is limited in scope
AllGenes provides the entire transcriptome for a wide variety of human and mouse tissues
Needs to provide high quality genes
PlasmoDB provides the entire Plasmodium genome.
Integrating EST, SAGE, and microarray data
EPConDB provides integration of EST and microarray gene expression data for a specific system
Will provide microarray analysis

47. Acknowledgements http:www.cbil.upenn.edu CBIL: Chris Overton
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
Fidel Salas
Juergen Haas
Annotation collaborators:
Nikolay Kolchanov Alexey Katohkin
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
PlasmoDB collaborators:
David Roos Martin Fraunholz
Jesse Kissinger Jules Milgram
Ross Koppel, Monash U.
Malarial Genome Sequencing Consortium
(Sanger Centre, Stanford U., TIGR/NMRC)