1. Current and Future Directions
State of CBIL
Current and Future Directions

2. Computational Biology and Informatics Laboratory October, 2001

3. CBIL Research Gene Discovery Gene Regulation
EST analysis
Genomic sequence analysis
Gene Regulation
Microarray analysis
Promoter/ regulatory region analysis
Biological data representation
Data integration
Ontology

4. CBIL: Gene Discovery Gene Annotation (Kolchanov) Gene coding potential
Gene function prediction
AllGenes
EPConDB (Kaestner, Permutt, Melton)
StemCellDB/ StroCDB (Lemischka, Moore)
Mouse chromosome 5 (Bucan)
PlasmoDB (Roos, Kissinger)
ParaDB (Roos)
Posters

5. CBIL: Gene Regulation PaGE PROM_REC TESS
Pancreatic development (Kaestner)
TGF-B signaling (Bottinger)
Fetal globin expression in adults (Fortina)
Brain disease and injury (Eberwine, Meaney)
Endothelial cell function (Davies)

6. CBIL: Biological Data Representation
Genomic Unified Schema
RNA Abundance Database
Connecting to a brain atlas (Nissanov, Davidson)
Microarray ontology (MGED)

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

8. 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

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

10. 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

11. 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

12. Predicting Gene Ontology Functions

13. AllGenes

14. AllGenes Enhancements: Annotated Entries

15. AllGenes Enhancements: Genomic Data

16.
New site

17. Contig View OM Restriction Sites Microsatellites Self-BLAST NRDB-BLAST
SAGE Tags
EST/GSS
FullPHAT
GeneFinder
GlimerM
Annotation (chr2-TIGR)

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

19. 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).

20. EPConDB Pathway query

21. Microarray Analysis: PaGE

22. 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

23. Promoter Analysis: PROM_REC

25. CAP4 provided by Paracel
Acknowledgements
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
CAP4 provided by Paracel

27. CBIL: Future Directions
Sequence/
Sequence annotation
Gene expression
experiment
Proteomics,
Metabolomics
Pathways/
Networks