1. PSB RECAP: Genomic Hula
2002 Kaua’i, Hawaii

3. Na Pali Coast

4. Wailua and Jurassic Falls

5. Conference Site

6. Breakdown of PSB Schedule
Tutorials
2 Keynote Lectures
7 Diverse sessions
most of which had discussion sections
Poster session
featured insane 1 minute overview

7. Genome-Wide Analysis and Comparative Genetics
Goal: Bridge gap between comparative computational genomics and biology
Emphasis on algorithms for comparative mapping and new approaches to analysis

8. Genome-Wide Analysis and Comparative Genetics
Volkmuth and Alexandrov: Evidence for sequence-independent evolutionary traces in genomics data
fold: structural motif used as building block in proteins
traces: conservation of fold along the genome and genes that are correlated in expression are more apt to share a fold
use these traces to infer fold of protein and improve ability to predict biochemical function

9. Genome-Wide Analysis and Comparative Genetics
Goldberg, McCouch & Kleinberg: Constructing comparative genome maps with unresolved map order
stack algorithm to compute marker order by optimizing parsimony criterion
casts mapping as labeling problem: two genomes are base and target. Need to label the segments of the target using names of linkage groups from the base.
Algorithm is generic - no species specific information

10. Keynote Lecture 1: David Botstein “Extracting Biologically Interesting Information from Microarray”
Role of microarrays is to direct other experiments
“Charge Ahead!”
Major effects view -- opposite of hypothesis driven experiments
Interesting twist on talk given at Penn

11. Literature Data Mining for Biology
Most key information still free text in journals and comment fields of databases
Need for consensus evaluation

12. Literature Data Mining for Biology
Ding, Berleant, Nettleton and Wurtele: Mining Medline: abstracts, sentences or phrases?
Systematic comparison of using abstracts, sentences or phrases in automated mining information retrieval
Examined recall, precision, and effectiveness using these different units to mine interactions among biochemical terms based on term co-occurrences

13. Keynote Lecture 2: Rebecca Eisenberg: Bioinformatics, Bioinformation and Biomolecules: the role and limitations of patents
Interesting discussion about fine line with patents between protecting work and common good

14. Human Genome Variation: Disease, Drug Response and Clinical Phenotypes
Need for clinical phenotype annotations in analysis between genotype/haplotype and phenotype
Celera Infomercial on new high throughput SNP genotyping automated computer system
95% accuracy with fully automated QC system

15. Human Genome Variation: Disease, Drug Response and Clinical Phenotypes
Oliver, Rubin, Stuart, Hewett, Klein and Altman: Ontology development for a pharmacogenetics knowledge base
Issue of mapping ontologies to external data sources

17. Phylogenetic Genomics and Genomic Phylogenetics
Utilization of genomic order and information about duplication events
Wang, Jansen, Moret, Raubeson, and Warnow “Fast Phylogenetic Algorithms for the analysis of genome rearrangement data: an empirical study”
Presents 2 new methods of character coding for parsimony-based analysis of genomic rearrangement (i.e. gene orders as sequences of state characters)

18. Genome Pathway and Interaction Bioinformatics
Collection and organization of pathway information is critical and needs to be addressed
Hartemink et al.: Bayesian approach that incorporates genomic location and expression data
Idea of simplifying subset used in model

19. Genome Pathway and Interaction Bioinformatics: Discussion
Hartemink: need unification after proliferation of ideas
Shrager: fiction of pathways and stasis
Karp: comparative studies of pathways (convservation and divergence, correlation among pathways, idea of “pathway variance”)
need for systematic comparison of databases

20. Genome Pathway and Interaction Bioinformatics: Discussion
Andre: Use of ontologies, connection with text mining (Geneways)
Garvey: Biospice.org Simulation program for integrated circuit evaluation and in silico experiments

21. Proteins: Structure, Function and Evolution
Approaches to determine protein structure and function
New emphasis on evolutionary models to get at this problem

22. Proteins: Structure, Function and Evolution
Nielsen and Huelsenbeck: Detecting positively selected amino acid sites using posterior predictive p-values
elegant approach to detecting selected sites that may have structural or functional importance
phylogenetic mapping of mutations from multiple sequence alignment

23. Ab initio computations of protein structure - Harold Scheraga
Goal is to determine how interactions lead to folded structure (not to predict structure)
Modeling is based only on energy
Excellent results in CASP3 and CASP4
Not actually clear if this approach achieves it goal