Tatiana V. Tatarinova, PhD is a computational biologist with over 15 years of experience. She received her undergraduate degree in Theoretical Physics from the Moscow Engineering Physics Institute, earned her MSc in Physics from University of Utah, Salt Lake City, Utah, and a PhD in Applied Mathematics from the University of Southern California.
For eight years, Tatiana worked at Ceres, Inc., a local biotech company, where she became the inventor of 15 U.S. and European patents. After leaving Ceres, she established and led the Glamorgan Computational Biology Research group at the University of South Wales for four years. In 2013 Tatiana joined CHLA as an Associate Professor of Research.
Moscow Engineering Physics Institute, Moscow, Russia, degree in Theoretical Physics, 1992; University of Utah, Salt Lake City, Utah, MSc (Physics), 1995; University of Southern California, Los Angeles, CA, PhD (Applied Mathematics), 2006
Women in Mathematics International, Society for Computational Biology
E. Elhaik,T. Tatarinova, D. Chebotaryov, and Genographic Consortium, “Geographic Population Structure (GPS) of worldwide human populations infers biogeographical origin down to home village”, Nature Communications, 5, 30 April 2014.
A. Bolshoy, B. Saleh, I. Cohen, T.Tatarinova, “Ranking of prokaryotic genomes based on maximization of sortedness of gene lengths”, Journal of Data Mining in Genomics & Proteomics, 5 (1), 2014.
E. Elhaik, T. Tatarinova, A. Klyosov, D. Graur, “The "Extremely Ancient" Chromosome that Isn't: A Forensic Bioinformatics Investigation of Albert Perry's X-degenerate Portion of the Y Chromosomes” European Journal of Human Genetics, January, 2014.
E. Elhaik, M. Pellegrini, T. Tatarinova, “Gene expression and nucleotide composition are associated with genic methylation level in Oryza sativa” BMC Bioinformatics, January, 2014.
T. Tatarinova, A. Kryshchenko, M. Triska, M. Hassan, D. Murphy, M. Neely, A.Schumitzky, “NPEST: a novel method and a database for TSS prediction”, Quantitative Biology, 2014, 10.1007/s40484-013-0022-2
The Computational Biology Lab conducts analysis of bacterial toxicity, bacterial evolution, human genetics and functional annotation of genomes. Research activities involve the development of novel efficient algorithms and tools for biological data analysis using high-performance computing, focusing on statistical modeling; computational genomics; pharmacokinetics and pharmacogenomics; genome annotation; biomarker discovery; and transcriptomics.
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