1. Rationale for GUS Answer queries:
‘Identify all the “spots” on an array that represent genes on chromosome 1 that are predicted to be transcription factors’
‘Identify tissues that “express” at least half the components of the interleukin-6 pathway’
‘Identify image clones that represent genes for which there is evidence for expression in the pancreas’
Facilitate gene expression and pathways/networks analyses.
Datamining of diverse genomics data
Gene index (Gene centric view of biological data)
Pragmatic: Combine CBIL databases and thus unify effort.

2. GUS: Genomics Unified Schema
free
text
Ontologies
Genes, gene models
STSs, repeats, etc
Cross-species analysis
Genomic
Sequence GO
Species
Tissue
Dev. Stage
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

3. GUS Data Public sequence data Public mapping data Annotation
Genbank, SwissProt, Prodom, PFAM, UCSC Golden Path, TransFac..
Public mapping data
Radiation hybrid
Annotation
DoTS (Assembled transcribed sequences)
Gene predictions (via Plasmodium consortium collaborations)
Functional predictions (GOFunction)
Some comparative genomics (mouse/human)
Transcription Factor binding site and promoter analyses
data sets (some proprietary) from collaborators (primarily in RAD)

4. Light weight PERL object layer
GUS system
External
Datasources
Data
Integration
Computational
Annotation
Validation
Light weight PERL object layer
Data Warehouse
Annotators
interface
Browser
&
bioWidgets
Java Servlet
(views)

5. Light weight PERL object layer
GUS system
External
Datasources
Data
Integration
Computational
Annotation
Validation
Light weight PERL object layer
Data Warehouse
Annotators
interface
Browser
&
bioWidgets
Java Servlet
(views)

6. High Level Flow Diagram of GUS Annotation
Genomic
Sequence
mRNA/EST
Sequence
Clustering and
Assembly
ORNL Gene predictions
GRAIL/GenScan
BLAST/SIM4
Predicted
Genes
DOTS consensus
Sequences
Merge Genes
Gene/RNA cluster
assignment
Gene
Index
Gene families, Orthologs
Assign Gene Name,
Manual Annotation..
Predicted RNAs
Predicted Proteins
framefinder /
DIANA
BLASTX
PFAM,SignalP,
TMPred, ProDom, etc
BLASTP
Other Annotation
(EPCR,
AssemblyAnatomyPercent,
Index Key Words,
SNP analysis)
BLAST Similarities
Protein
Features/Motifs
Algorithms for
functional predictions
GO Functions

7. “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

8. Light weight PERL object layer
GUS system
External
Datasources
Data
Integration
Computational
Annotation
Validation
Light weight PERL object layer
Data Warehouse
Annotators
interface
Browser
&
bioWidgets
Java Servlet
(views)

9. Different Views of GUS
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*

11. Summary of AllGenes.org content

12. Boolean Query - Finding Surface Antigens
Can combine any of the ‘primitive’ queries on genes page.
Query forms are ‘bookmarkable’.

13. StemCellDB-GUS website

15. Light weight PERL object layer
GUS system
External
Datasources
Data
Integration
Computational
Annotation
Validation
Light weight PERL object layer
Data Warehouse
Annotators
interface
Browser
&
bioWidgets
Java Servlet
(views)

16. Assembly Validation Alignment to Genomic Sequence via Blast/sim4.
preliminary data look good
Assembly consistency

17. GUS: Genomics Unified Schema
Ontologies
Genes, gene models
STSs, repeats, etc
Cross-species analysis
free
Genomic
Sequence
text GO
Species
Tissue
Dev. Stage
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
Domains
Function
Structure
Cross-species analysis
Protein
Sequence
Pathways
Networks
Representation
Reconstruction
under development

18. RAD GUS TESS EST clustering and assembly Identify shared
Genomic alignment
and comparative
Sequence analysis
Identify shared
TF binding sites

19. Acknowledgements CBIL: Chris Overton Brian Brunk Jonathan Crabtree
Sharon Diskin
Steve Fischer (Doubletwist)
Mark Gibson (GeneLogic)
Greg Grant
Elisabetta Manduchi
Joan Mazzarelli
Debbie Pinney
Angel Pizarro
Jonathan Schug
Jian Wang (Celera)
Chris Stoeckert
PlasmoDB collaborators:
David Roos Martin Fraunholz
Jesse Kissinger Jules Milgram
Dan Lawson (Sanger)
Ross Koppel (Monash U.)
Malaria Genome Consortium
Allgenes.org collaborators:
Ed Uberbacher, ORNL
Doug Hyatt, ORNL
EPConDB collaborators:
Klaus Kaestner Marie Scearce
John
Doug Melton, Harvard
Alan Permutt, Wash. U
Comparative Sequence Analysis Collaborators:
Maja Bucan Tim Wiltshire
A. Lengeling, L. Tarantino,
S. Kanes
Whitehead/MIT Center for Genome Research

20. StemCellDB Architecture
All sequences get entered into flat file db first with efficient mechanism for filtering to public vs private
Ones marked public get incoorporated into GUS and at regular intervals also submitted to dbEST (GenBank)
Private sequences and ones not dealt with will stay in flat file db (current system).
Public sequences will be removed from flat file db to decrease overhead and query times.
StemCell static pages should be maintained by Princeton
Automated annotation applied by CBIL and manual annotation in StemCell flat files moved over to GUS by semi-automatic methods
CBIL annotators may prioritize StemCellDB entries for annotation