# Presentation: 234914

## Year One Milestones 

**For the VIMSS Comparative Genomics Pipeline **
**Eric Alm**
**EJAlm@lbl.gov**

## Outline

- Comparative Genomics Database
- Over 125 genomes
- Perl-based API
- Over 600 genomes expected soon!
- Protein Annotation Web Tools
- Comparative Genomics Browser
- Genome Annotation
- Operon prediction
- Regulon prediction
- Motif detection
- Data Analysis
- Prokaryotic gene expression database
- Integration with FGC

## Protein Annotation Pages

- Release Date: 8/15/2003

**Notes:**
How this can be used-- and what we will have in the future--- links to experimental data, interactions,cis-regulatory sequence etc.

## Comparative Genomics Browser

## Genome Annotation

- Operon prediction
- ‘Parameter-free’ prediction tool for uncharacterized genomes
- Survey of Operon structure in prokaryotes
- Evolution of operons
- ‘Operons Early’ scenario
- Regulon prediction
- DNA motif detection
- Structure based Protein-DNA interface modeling/design

## Operon Prediction

- Why predict operon structure?
- Functional annotation
- Evolutionary questions
- Few genomes with experimental data
- Computational Approaches
- Microarray coexpression (Liao* et al.)*
- Metabolic pathways (Kasif *et al.*)
- Intergenic length (Collado-Vides *et al.*)
- Gene clusters (Salzberg *et al.*)

## Outline of Approach

- Calculate number of operons (Nop) in genome
- Based on the number of direction changes
- Verify with intergenic length distributions
- For each pair of genes calculate log-likelihood that the genes are on the same operon
- Find the optimal map (based on pairwise score) that partitions the genome into Nop operons

## Scoring Function

- Intergenic length
- The most common separation is 1bp or 4bp overlap
- TGATG
- ATGA
- Large intergenic lengths rarely observed
- mRNA instability

## Scoring Function

- Gene Neighbors
- Gene pairs that tend to occur physically nearby on the chromosome in many divergent genomes

## Gene Neighbors

- Intergenic length distributions for “true positives” (highly conserved gene neighbors) and combined (same direction) sets

- Log-likelihood scores depend on “true positive” distribution, “true negative” distribution, combined distribution, and prior knowledge of Nop

- Calculating GNM score
- Orthologs are defined as Bidirectional Best BLAST hits
- The probability of two genes being neighbors by “chance” is estimated from the fraction of adjacent genes on convergent transcripts that are neighbors
- Related genomes are clustered such that (Pchance < eps)
- Only one genome per cluster can contribute to the total score to avoid overestimating significance

## Scoring Function

- Phylogenetic profiles
- Genes that co-occur in the same genomes tend to be functionally related

- Taken from Marcotte *et al.,* 1999

## Scoring Function

- Intrinsic termination signals
- Rho-independent terminators can be detected using RNA folding algorithms (RNAfold) in some organisms

## Operon Prediction

- Limitations
- Alternative transcripts
- Limited by accurate prediction of start codon
- ‘Parameter-free’ version requires accurate Codon Adaptation Index
- Preliminary Benchmarks in *E. coli*

- Scoring function

- Accuracy

| Intergenic length only | 0.81 |
| --- | --- |
| Length + Phylo | 0.83 |
| Length + GNM | 0.86 |
| Length + Phylo + GNM | 0.87 |

- Based on experimentally
- verified *E. coli *operons
- Accuracy =
- TP+TN/TP+TN+FP+FN

## Regulon (?) Prediction

- Gene Neighbors

- Phylogenetic Profiles

## cis-element Prediction

- Whole genome methods
- Moby Dick
- DimerFinder (Results in Biofiles for Dv, Gm, So)
- Predicted regulon methods
- AlignACE
- GIBBS-sampler
- MEME
- Phylogenetic footprinting
- What genomes to use?

**Notes:**
Dorothea thinks that Jun Li’s motif regressor is very powerful-- have you used it?
In any case, you might explain here what the basis of these different tools are

## Choosing Genomes for Phylogenetic Footprinting

- Fully sequenced gamma-proteobacteria

## Integration with the Functional Genomics Core

- Prokaryotic Gene Expression DB
- Currently no central repository
- Growing amounts of data in many species
- Comparative  analysis of gene expression
- Evaluating predicted ‘Regulons’
- Correlating gene expression with genome structure

## Prokaryotic Gene Expression DB

- >20 organisms
- >35 different treatments
- Expecting ~90 publications this year
- Currently >820 experiments in our DB

**Notes:**
Where did you get the graph?

## Comparative Analysis of Gene Expression

- Heat shock response in two species

## Comparative Analysis of Heat Shock Response

- 5% most up-regulated genes in both species

**Name****	****Description**
- b1664	possible enzyme
- clpB	heat shock protein
- dnaJ	chaperone with DnaK; heat shock protein
- dnaK	chaperone Hsp70; DNA biosynthesis; autoregulated heat shock proteins
- ftsJ	cell division protein
- fucP	fucose permease
- grpE	phage lambda replication; host DNA synthesis; heat shock protein; protein repair
- hflB	degrades sigma32, integral membrane peptidase, cell division protein
- hslV	heat shock protein hslVU, proteasome-related peptidase subunit
- htpG	chaperone Hsp90, heat shock protein C 62.5
- hybC	probable large subunit, hydrogenase-2
- ibpA	heat shock protein
- lon	DNA-binding, ATP-dependent protease La; heat shock K-protein
- miaA	delta(2)-isopentenylpyrophosphate tRNA-adenosine transferase
- mopA	GroEL, chaperone Hsp60, peptide-dependent ATPase, heat shock protein
- mopB	GroES, 10 Kd chaperone binds to Hsp60 in pres. Mg-ATP, suppressing its ATPase activity
- rpoD	RNA polymerase, sigma(70) factor; regulation of proteins induced at high temperatures
- rpoH	RNA polymerase, sigma(32) factor; regulation of proteins induced at high temperatures
- rseA	sigma-E factor, negative regulatory protein
- yaiU	putative flagellin structural protein
- ybbN	putative thioredoxin-like protein
- ybeD	orf, hypothetical protein
- ycdQ	orf, hypothetical protein
- yfjI	orf, hypothetical protein

## Detecting cis-regulatory motifs

## Evaluating Predicted Regulons

*E. coli* data for 14 conditions
- Blattner Lab
- Regulons are nearly as tightly correlated as operons
- Operon structure in distantly related species can be used to infer coregulation

**Correlation**

**Correlation**

*Shewanella* data for 4 conditions
- ORNL
- Regulons are significantly more correlated than random, but not as tightly correlated as for *E. coli*
- Fewer conditions
- No mutant regulator data

**Notes:**
Is this E. coli or is it Shewanella...Don’t you want to show the comparison?

## Gene Expression and Genome Structure

- Test Case - Cyanobacteria
- Circadian Rhythms
- Expression of nearly all genes is tied to 24-hour cycle
- Heterologous genes/promoters adapt to host cycle
- Clock gene has homology to Helicase/Recombinase
- Genome Structure
- Little known about DNA replication
- Very little GC-skew - no obvious peak
- Little conservation of gene order/operons

## Gene Expression and Genome Structure: Cyanobacterial Clocks

## Summary

- Web based comparative genomics tools
- Operon predictions
- Gene interaction predictions
- Gene neighbors (Regulons?)
- Phylogenetic profiles
- Correlated microarray expression
- Release Date: 8/15/2003
- Operon prediction tool
- Insights into the evolution of operons
- Regulon predictions
- Gene neighbors
- Phylogenetic profiles
- Coexpressed genes
*cis*-regulatory motif detection
- Dimer-based method
- Comparative Gene Expression DB
- Preliminary comparative analysis of microarray data

## Future Directions

- Automated input pipeline for new genomes
- Complete ‘parameter-free’ operon prediction tool
*cis*-regulatory motif predictions
- Regulon-based methods
- Phylogenetic profiling
- Integrate experimental data into protein annotation pages
- Microarray
- Proteomics
- Gene deletion
- Construct DB queries over experimental results via website
- Other ideas?
- Release Comparative Gene Expression DB
- Work with Functional Genomics Core to add genomic context to high-throughput data

## Acknowledgements

- Director
- Adam Arkin
- VIMSS - Arkin Lab
- Katherine Huang
- Richard Koche
- Sarah Wang
- Dubchak Lab
- Simon Minovitsky
- Volunteer
- Vladmir Ulyashin

- ORNL
- Jizhong Zhou
- Matthew Fields
- Dorothea Thompson
- Yongqing Liu
- Adam Leaphart
- Haichun Gao
- and others...

**Notes:**
You probably want to insert a slide on summarizing your accomplishments and what will happen over the next year and how it will serve GTL. Ending on cyanobacteria is off the GTL topic and you have to get back to touch base.