Ramana Davuluri
Assistant Professor
Department of Molecular Virology, Immunology & Medical Genetics
Tzagournis Medical Research Facility
420 W 12th Ave
Columbus , OH 43210
Phone: (614) 688-3088
ramana.davuluri@osumc.edu

Research Interests
The bioinformatics and computational biology laboratory of Dr. Davuluri is an integrated team is focused on the development of statistically rigorous computational tools that will, hopefully, accelerate research in human cancer genetics and eventually translate into the clinical setting. Topics of interest currently pursued can be broadly categorized into the following three areas:

Genome wide discovery and analysis of alternative promoters in different tissues, cell types and developmental stages: Promoters located at the 5' ends of genes play a critical role in the regulation of transcriptional initiation. Emerging evidence suggests that a significant fraction of the ~35,000 human genes likely contain alternative promoters, which produce more elaborate regulation of gene expression in different tissues, cell types and/or developmental stage. We are developing a comprehensive approach toward the identification and characterization of the alternative promoters of gene loci. The combination of computational, statistical and experimental approaches will hopefully provide insight into the characterization of important regulatory regions.
Computational tools to characterize transcriptional regulation in hematopoiesis: Extensive molecular research in hematopoietic development has characterized transcription factors (TFs) and their binding sites in target gene promoters, however the information is dispersed heterogeneously throughout published literature and public databases. We are integrating these data into one unified resource, entitled HemoPDB. The availability of important regulatory information provides the foundation to characterize regulatory networks and develop statistical models to predict TF binding sites and synergistic relationships in hematopoietic promoters. We are hopeful that our bioinformatics approach will contribute toward the unraveling of normal transcriptional regulation in hematopoiesis and aberrances that lead to leukemogenesis.

The development of robust databases and visualization tools for genome data and associated annotations: The completion of the entire genome assemblies of model organisms has imposed one of the next major targets for both the biology and bioinformatics communities -- the characterization of the whole gene regulatory network. The accomplishment of this goal largely depends on the the development of large-scale promoter annotation databases, with efficient web interfaces that are useful to the scientific research community. With the utilization of publicly available data and computational tools, we develop databases largely focused on promoter annotation. We also develop efficient visualization software that redefines pre-existing modes of genomic presentation.
For more information about our ongoing research, please visit: http://bioinformatics.med.ohio-state.edu

Education & Training

Nagarjuna University, Guntur, India 1988 B.S.

Indian Agriculture Statistics Research Institute, New Delhi, India 1991 M.S.

Indian Agriculture Statistics Research Institutes, New Delhi, 1996 Ph.D.

1998 Computational Post Doctoral Fellow, Deptartment of Plant Genetics, VIB, University of Ghent, Ghent, Belgium

1999-2001 Computational Post Doctoral Fellow, Zhang Lab, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY

Select Publications (Bioinformatics & Computational Biology)

1. Sun, H. and Davuluri, R.V. (2004) Java-based Application Framework for Visualization of Gene Regulatory Region Annotations. Bioinformatics. 20:727-734.

2. Pohar, T.T., Sun, H. and Davuluri, R.V. (2004) HemoPDB: Hematopoiesis promoter database, an information resource of transcriptional regulation in blood cell development. Nucleic Acids Res. 32:D86-90. (Note: This database was featured in Science, 303: 291 (in NetWatch, Database: Bad Blood, January 16, 2004).

3. Davuluri, R.V. and Zhang, M.Q. (2003). Computer software to find genes in plant genomic DNA. Methods Mol Biol., 236:87-108.

4. Davuluri, R.V. Sun, H. Palaniswamy, S.K. Matthews, N. Molina, C. Kurtz, M. and Grotewold, E. AGRIS: (2003) Arabidopsis Gene Regulatory Information Server, an information resource of Arabidopsis cis-regulatory elements and transcription factors. BMC Bioinformatics, 4:25.

5. Davuluri, R.V., Grosse, I. and Zhang, M.Q. (2001). Computational identification of first exons and promoters in the human genome. Nature Genetics, 29: 412-417. (Note: This work was featured in Nature Reviews Genetics, 3: 3-9; in Bioinformatics section of Highlights as “Filling the gap in gene prediction” January, 2002)

6. Tabaska, E. Jack, Davuluri, R.V., and Michael Q. Zhang (2001). A novel 3'-Terminal exon recognition program for Human DNA Sequences. Bioinformatics 17: 602-607.

7. Davuluri, R.V., Suzuki, Y., Sugano, S. and Zhang, M.Q. (2000). CART classification of human 5'UTR sequences, Genome Research 10: 1817-1827.

8. Pavy, N., Rombauts, S., Dehais, P., Mathe, C., Davuluri R.V., Leroy, P. and Rouze, P. (1999). Evaluation of gene prediction software using a genomic dataset: Application to Arabidopsis Thaliana sequences. Bioinformatics 15: 887-899.

Select Publications (Cancer Genomics & High-throughput Technologies)

9. Aldred, M.A., Huang, Y., Liyanarachchi, S., Pellegata, N.S., Gimm, O., Jhiang, S., Davuluri, R.V., de la Chapelle, A., Eng, C. (2004). Papillary and follicular thyroid carcinomas show distinctly different microarray expression profiles and can be distinguished by a minimum of five genes. J. Clinical Oncology, (Accepted).

10. Stanchina, E.D., Querido, E., Narita, M., Davuluri, R.V., Pandolfi, P.P., Ferbeyre, G. and Lowe, S.W. (2004) PML as a direct p53 target that modulates p53 effector functions. Molecular Cell. 13: 523-535.

11. Besco, J.A., Popesco, M.C., Davuluri, R.V., Frostholm, A.M. Rotter, A. (2004) Genomic structure and alternative splicing of murine R2B receptor protein tyrosine phosphatases (PTP kappa, mu, rho and PCP-2). BMC Genomics, 5: 14.

12. Baldus, C.D., Liyanarchchi, S., Mrózek, K., Auer, H., Tanner, S.M., Guimond, M. Ruppert, A.S., Mohamed, N., Davuluri, R.V., Caligiuri, M.A., Bloomfield, C.D. and de la Chapelle, A. (2004) Acute myeloid leukemia with complex karyotype: amplification of two chromosome 21 regions discloses overexpression of app, ets2 and erg genes. Proc Natl Acad Sci USA 101:3915-3920.

13. Rush, L.J., Raval A., Funchain P., Johnson, A.J., Smith, L., Lucas, D., Bembea, M., Liu, T., Heerema, N.A., Rassenti, L., Liyanarachchi, S., Davuluri, R.V., Byrd, J.C., Plass, C. (2004) Epigenetic profiling in chronic lymphocytic leukemia reveals novel methylation targets. Cancer Res. 64:2424-33.

14. Borrego, S., Wright, F.A., Fernández, R.M, Williams, N., López-Alonso, M., Davuluri, R.V., Antiñolo, G., Eng, C. (2003) A founding locus within the RET proto-oncogene may account for a large proportion of apparently sporadic Hirschsprung disease and a subset of sporadic medullary thyroid carcinoma cases. Am J Hum Genet 72:88-100.

15. Nahle, Z., Polyakoff, J., Davuluri, R.V., Jacobson, M.D., McCurrach, M.E., Narita, M., Zhang, M.Q., Lazebnik, Y., Bar-Sagi, D. and Lowe, S.W. (2002). Direct coupling of the cell cycle and cell death machinery by E2F. Nat Cell Biology, 4:859-64.

16. Yoon, H., Liyanarchchi, S., Wright, F.A., Davuluri, R.V., Lockman, J., de la Chapelle, A., and Pellegata, N.S. (2002) Gene expression profiling of isogenic cells with different TP53 gene dosage reveals that many genes are affected by TP53 dosage and identifies CSPG2 (Versican) as a novel direct target of p53. Proc Natl Acad Sci. USA, 99:15632-15637.

17. Dai, Z., Weichenhan, D., Wu, Y.Z., Hall, J.L., Rush, L.J., Smith, L.T., Raval, A., Yu, L. Kroll, D., Muehlisch, J. Frühwald, F.C., de Jong, P., Catanese, J., Davuluri, R.V., Smiraglia, J.D. and Plass, C. (2002) An AscI boundary library for the studies of genetic and epigenetic alterations in CpG islands. Genome Res., 12:1591-1598.

Select Publications (Statistics)
18. Gupta, V.K., Ramana, D.V.V. and Parsad, R. (2002). Weighted A-optimal block designs for comparing test treatments with controls with unequal precision. J. of Statistical Planning and Inference, 106:159-175.

19. Gupta, V.K., Ramana, D.V.V. and Parsad, R. (1999). Weighted A-efficiency of block designs for making treatment-control and treatment-treatment comparisons. J. of Statistical Planning and Inference, 77:301-319.

20. Gupta, V.K., Ramana, D.V.V. and Agarwal, S.K. (1998) Weighted A-optimal row-column designs for making treatment-control and treatment-treatment comparisons. J. Combinatorics, Information and System Sciences, 23(1-4), 333-344.