Principal Investigator, Associate Professor of Research Pediatrics
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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.

Education

Medical School: 

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

Accomplishments

Medical Memberships: 

Women in Mathematics International, Society for Computational Biology

Publications: 

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

Research Interests: 

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.

Research Overview 

  • Development of novel methods for analysis of bio-medical data, such as genome annotations, computational ancestry prediction for personalized medicine, bacterial toxicity, and cancer biomarkers
  • Prediction of methylation levels from other genomic features
  • Development of algorithms to support Barcode-of-Life initiative dedicated to supporting the development of DNA barcoding as a global standard for species identification

Key Research Highlights and Findings

Together with researchers from the National Geographic Genographic project, Tatarinova developed a novel algorithm, Geographic Population Structure prediction, to accurately predict geographic origin of individuals worldwide. With the GPS tool, they are able to take unknown samples, identify the proportions of admixture (genetic characteristics specific to certain ethnic groups that were combined because of events like migration or invasion), and then calculate the distance to the nearest known population that shares the same admixture signature, in order to identify place of origin.

According to the researchers, in ethnically diverse regions like the United States, where many people know only a few generations of their descendants, this kind of screening has important medical implications. Discovery of certain genotype might indicate the potential for a genetic disease and suggest that diagnostic testing be done. Also, as scientists learn more about personalized medicine, there is evidence that specific genotypes respond differently to medications—making this information potentially useful when selecting the most effective therapy and appropriate dosing. The investigators are currently designing a study to correlate pharmacokinetics, the time course of drug metabolism, with genotype.

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Genealogy and Biogeography Meet Personalized Medicine

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