1. Detecting Orthologs Using Molecular Phenotypes a case study: human and mouse Alice S Weston

2. What is a “molecular phenotype”? mRNA expression patterns detected using microarray techniques can reveal the co-expression of two genes in the same tissue or sample

3. Hypothesis It is speculated that orthologous genes between Human and Mouse will be co- expressed with a similar set of partners compared to a pair of non-orthologous genes that are similar at the sequence level.

4. Why do we care? categorize the biological function of mammalian core proteins learn about the similarity of genes with little sequence homology see how orthologous genes have changed since their divergence

5. evolutionary sequence changes can mask orthologs coding region active site = mutation BLAST hit #1 paralog BLAST hit #2 true ortholog!

6. Methods calculated Spearman correlations used z-Fisher transform because of missing data—consider dimension built co-expression neighborhood for each human gene (center), 100 each found related genes in neighborhoods of top two BLAST hits in mouse for the central human gene

7. Example 10270h 6814 7375 51763 8888 9646 9092 20912 54387 54194m 22258 19062 22083 56399m 20912 54387 20227 22258 20227 19062 22083 BLAST hit #1BLAST hit #2

8. Methods (cont.) determined the ranks of the neighbors in relation to their central mouse gene used a sign system to tell which mouse gene was more co-expressed with the human gene asked: Are there any instances where BLAST hit #2 is the true ortholog?

9. Results

11. cases where the second best mouse gene in BLAST has the best co-expression with the human gene the best mouse gene in BLAST has the best co-expression with the human gene ~62.9% of the time results look promising if sample size is increased

12. problems along the way…. not enough microarray co-expression data to rank most of the neighbors some human genes have no predicted orthologs in mouse— for building mouse neighborhoods limited sample size: some human genes do not have two orthologs in mouse— to test hypothesis

13. ….solutions do more microarray experiments, increase amount of data compare human genes to another species with more known orthologs

14. Acknowledgments Josh Stuart Reading: Barak A Cohen, Yitzhak Pilpel, Robi D. Mitra, and George M. Church. (2002) Discrimination between Paralogs using Microarray Analysis: Application to the Yap1p and Yap2p Transcriptional Networks. Molecular Biology of the Cell. 13, 1608 – 1614.

15. Happy Spring Break!!!