1. Center for Integrated Fungal Research
Fungal Genomics Laboratory

2. Industrial applications
The Center for Integrated Fungal Research investigates economically important fungi
glutamic acid
citric acid
amylases
proteases
lipases

3. Bioterrorism
Given recent national and world events, the biological and informatics aspects of fungal research, can be seen to have immediate and real applications.

4. Biologically interesting and genetically tractable
With seven chromosomes and approximately 40 Mega bases for the genome, Magnaporthe grisea, is a relatively simple eukaryote. In this small package, we have multi-cellular, pathogenic organism that spans almost the whole range of interesting research areas. Consequently, it is model organism for study.
Insight into eukaryotic gene regulation and development

5. Framework of rice blast genome
Deep (25X) large insert (130 kb) single enzyme (HindIII) BAC library from rice infecting strain – 9,216 clones
B. BAC fingerprints used to create contigs
RFLP 1
RFLP 2
RFLP 3
BAC 1
C. BAC contigs anchored to genetic map
BAC 2
BAC 3
A. BAC-end sequence provides “Sequence Tag Connectors”
BAC 4
The biological foundation work has already been done. This sets the stage for informatics analysis.
BAC 5
BAC 6 1f 2f 3f 4f 5f 6f 1r 2r 3r 4r 5r 6r
STC: ~500 bp sequence every 3-4 kb across genome

6. USDA-IFAFS project Oct 2000
“Gene discovery in the rice blast fungus: ESTs and sequence of chromosome 7”
1. Generate ~5 X draft sequence of chromosome 7 (4.2 Mb).
2. Generate 35,000 ESTs and create a set of ~5,000 ESTs representing unique genes.
3. Provide basic sequence analysis and integration of data into physical map of chromosome 7.
With this foundation, initial techniques for high throughput analysis have been worked out.

7. NSF-IFAFS project Oct 2001 whole genome sequence
host-pathogen function analysis
Generate ~7 x draft sequence of M.grisea
Generate 50,000 knockouts
Analyze host-pathogen interaction
Provide basic sequence analysis
Now we scale up the processing. We are currently engaged in a high throughput, whole genome, shotgun sequencing effort. We are starting to gather the information for a high throughput, biologically based functional identification.

8. Consequences of Scaling
Moore’s law has allowed labs to keep ahead of data
Sequence data is now outpacing processing capability
Bioinformatics processing will be a real problem
The consequences of success, and the advancing state of genomic science, is that we are being overwhelmed with Bioinformatic processing needs.

9. Computational platforms
Modern biology requires robust computational platforms
Computer technology implementation is expensive (from a biologists viewpoint)
Computer technology development is even more expensive (you want how much?!)
This detracts from research for small labs
All of this adds up to labs dedicating an ever increasing amount of scarce research funds to the informatics aspect of Bioinformatics.

10. On the brink
Significant investment in off the shelf components and cross training people
Moderate sized genomes
20 to 50 Mega Bases
Takes 2 weeks for initial analyses
Homology searches take days
To date, labs have been able to keep ahead of the information glut by scrabbling for and cobbling together available resources. It is clear, that given the trends, we are on the brink of entering chaos.

11. Local blast (www.fungalgenomics.ncsu.edu)
Over the past few years, CIFR has been developing in-house tools to try and reduce the work load.

12. Federated database Select a chromosome
Link to genetic information (blue)
And to tie together disparate information sources
Link to marker data and other data at

13. High Throughput Genomic Processing and Display
As well as using available tools to position ourselves for the era of high throughput analysis

14. Rice blast N. crassa synteny
2 kb
3 kb
N. crassa Contig 1.515
10 kb
185kb
0.5 kb
M. grisea - BAC 6J18
111kb
15 kb
20 kb
1 kb
N. crassa Contig 1.13
1 kb
And yet, the latest research analysis are still hand crafted. This approach is not viable for high throughput analysis.
N. crassa Contig 1.513
17 kb
N. crassa Contig 1.841
97 out of 179 unique ESTs from chromosome 7 gave significant (E<10-5) tBlastX match to N. crassa genome shotgun assembly

15. CIFR BioInformatics Genbank BioInformatics Foundation Rube Sequence
Public
Http
Exposure
Rube
Sequence
Pipe line
Sequence
Data
Biological
results
GRL
High
Throughput
WebBlaster
AlkaEST
Data
mining
mask
Phred
consed
Phrap
Http
Blast
Report
Blast
Report db
Artemis
Curation
Relational
Data Model
BioInformatics
Advanced
Data
Loading
Genome
BioPerl
Interface
browser
Curation
Work area OO
Genomic
Analysis
extract
Submissions
Extraction
The final result ends up looking a lot more like a computer data processing shop than a Biological research center.
Genbank
load
Higher Order
BioInformatics
homology
BioInformatics
Research
PBS/LSF
Grid Access
Repeat analysis
Gene prediction
Developed at
CIFR
EST analysis
synteny
Cluster analysis
Ongoing work
at CIFR
Pathway analysis
NC BioInformatics
Super computing
Grid
In-silico mutation
Open source
and others
Cellular models

16. And over the . . . edge Our whole genome arrives Spring 2002
Everyone wants immediate results
Host (Rice) genome size far greater than the pathogen
Comparative genomics likely to require N way analyses
And then there’s proteomics ….
It’s clear that we have an immediate need for the North Carolina Bioinfomatics Grid.

17. Research Biology NCSU GRL Romulus Remus Excellent foundation work
Genomic biology research has gone through a transition from the research scale operation
~6 years
to sequence
M.grisea

18. Industrial Scale Biology
High
Throughput
Sequence
Centers
(Whitehead)
To an industrial scale operation.
~4 days
to sequence
M.grisea

19. Research Bioinformatics
CIFR FGL
Mycelial mat
Excellent
foundation work
Research Bioinformatics is at the same point regarding research capabilities
est. 4 years
to analyze
M.grisea

20. Industrial Scale Bioinformatics
North
Carolina
BioGrid
Now we all have a need to scale up to industrial levels
Hopefully
4 hours
to analyze
M.grisea

21. Islands of Capability
There are not enough resources for every lab to re-implement technologies
Individual centers specialize according to their research focus
Grid ties together disparate systems
Share knowledge and capabilities
Standards based for interoperability
It’s clear that research labs can not afford to re-create the computing resources. Rather, each lab has areas in which they have excellence. The BioGrid will allow the cross utilization of these capabilities. As techniques are refined, they can be moved into the grid as globally available resources.

22. Future directions 5 years*
Organized distributed research - “Virtual Centers”
Bioinformatics
Tool development
Gene prediction algorithms for filamentous fungi
Gene Indexing
“Distributed Annotation Systems (DAS)”
Develop better search features “Queries”
Integrate sequenced and annotated BAC clones
Integrate ESTs and expression profiles etc
Functional Genomics
Comparative studies - saprophyte vs pathogen etc
Coordinate IRBGC and PGI etc
Complete nucleotide sequence, full length ESTs
Knock out/silence all genes
Transcriptional profiling in various backgrounds (path mutants)
Construct protein-protein linkage maps (signaling pathways)
There are differences between a biologists out look
* The biologists view

23. Future Directions 5 years*
Collaborative knowledge sharing
New data mining approaches
New ways of visualizing the information
In-silico experimentation
Gene knock outs
Regulatory modification
Pathway models
Cellular models
And the informaticians outlook. Fortunately, there is a large amount of overlap. This bivalent view of the future prvides fertile ground for synergy to occur.
* The bioinformaticians view

24. Finding solutions to practical problems
Seeking answers requires asking questions
Takes 1-2 weeks per question
BioGrid may give near real-time response
BioGrid will bridge the islands of capability
Focus resources back on our work
Consequently, we are going to further accelerate the rate of discovery