1. A systems-biology approach for finding TSS-specific transcriptional regulation in the same gene Yishai Shimoni Andrea Califano Lab Columbia University

2. Zhao X et al. (2009) Dev Cell. 17(2):210-21. Mani KM et al. (2008) Mol Syst Biol. 4:169 Palomero T et al., Proc Natl Acad Sci U S A 103, 18261 (Nov 28, 2006). Margolin AA et al., Nature Protocols; 1(2): 662-671 (2006) Margolin AA et al., BMC Bioinformatics 7 Suppl 1, S7 (2006). Basso K et al. (2005), Nat Genet.;37(4):382-90. (Apr. 2005) Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39 Zhao X et al. (2009) Dev Cell. 17(2):210-21. Wang K et al. (2009) Pac Symp Biocomput. 2009:264-75. Mani KM et al. (2008) Mol Syst Biol. 4:169 Wang K et al. (2006) RECOMB Basso et al. Immunity. 2009 May;30(5):744-52. Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40. Lefebvre C. et al (2010), Molecular Systems Biology, Jun 8;6:377. Carro MS et al. (2010) Nature Jan 21;463(7279):318-25 Mani K et al, (2008) Molecular Systems Biology, 4:169

3. ARACNe: Reverse Engineering Regulatory Networks Computing Mutual Information Start with a large collection of Microarray Gene Expression Profiles Select two genes, a TF and a candidate target t: Use expression across multiple experiments to measure Target t TF

4. ARACNe: Reverse Engineering Regulatory Networks Computing Mutual Information Start with a large collection of transcription start site (TSS) activity levels Select a TF and a candidate target TSS t: Use expression across multiple experiments to measure Target t TF

5. ARACNe TF 2 Target TF 1 Filtering indirect interaction: applying Data Processing Inequality TF 1 TF 2 Target X

6. ARACNe TF 1 T1T1 T1T1 TF 2 TF N TMTM TMTM T2T2 T2T2 Compute all pairwise Mutual Information (One of the two nodes must be a TF) TF 1 T1T1 T1T1 TF 2 TF N TMTM TMTM T2T2 T2T2 Remove Non Statistically Significant Interactions TF 1 T1T1 T1T1 TF 2 TF N TMTM TMTM T2T2 T2T2 Apply the DPI

7. Challenges in applying ARACNe to FANTOM5 data Normalization Joining tag counts into TSSs Using multiple tissues together

8. MA plot of original tag counts shows need for normalization

9. Top rank-invariant locations follow power-law relation

10. MA plot of normalized TSS activity shows good normalization Note: samples were NOT normalized compared to each other, but each one compare to reference

11. Good normalization observed in multiple random pairs

12. Samples are separated by tissue

13. Correlation clustering shows significant negative correlations Strong Negative Correlation Hematopoietic Assorted samples

14. Possible solution to tissue driven mutual information Calculate MI only form the subset of samples in which both the TF and the target TSS are expressed

15. In spite of caveats MI agrees with chip-seq http://amp.pharm.mssm.edu/lib/chea.jsp

16. Interrogating Gene regulatory networks Analyzing data using ARACNe network

17. MARINa: Master Regulator Inference analysis Repressed TF x Targets Activated TF x Targets TF x TF Regulon Over-expressed in Ph2 vs. Ph1 Under-expressed in Ph2 vs. Ph1 If TF x were a Master Regulator of Ph1→Ph2 transformation, then its regulated genes should distribute as follows: A Master Regulator is a gene that is necessary and/or sufficient to induce a specific cellular transformation or differentiation event. Phenotype 2 Phenotype 1 TF x ? Lefebvre C. et al (2010), Molecular Systems Biology, Jun 8;6:377. Carro MS et al. (2010) Nature Jan 21;463(7279):318-25 Gene Expression Repressed TF x Targets Activated TF x Targets

18. Differentially regulated same-gene TSS TSS1 TSS2

19. Methylation data does not explain alternative TSS usage CD34+ Adult kidney Fetal lung Pancreatic Islets Smooth Muscles http:// www.genboree.org/epigenomeatlas TSS1 TSS2

20. MARINA initial results for ARHGAP24 differential TSS activity

21. Future directions Apply ARACNe to the whole dataset to find differential regulation of TSS activity in the same gene Apply MARINA to find master regulator of mutually exclusive TSS in the same gene Use additional algorithms to analyze regulatory networks in developmental stages, and in the time-course data

22. Acknowledgments FANTOM5 Andrea Califano Mukesh Bansal Mariano Alvarez Maria Rodriguez Martinez Gonzalo Lopez Garcia