Open Problems of Computational Immunogenomics - Yana Safanova

Computer Science Seminar Series
February 12, 2021
“Open Problems of Computational Immunogenomics”
Yana Safanova, University of California, San Diego
Twelve years ago, biologists developed the repertoire sequencing technology that samples millions out of a billion constantly changing antibodies, or immunoglobulins, circulating in each of us. Repertoire sequencing represented a paradigm shift as compared to previous “one-antibody-at-a-time” approaches; raised novel algorithmic, statistical, information theory, and machine learning challenges; and led to the emergence of computational immunogenomics. Yana Safonova will describe her recent work on reconstructing the evolution of antibody repertoires, inferring novel diversity (D) genes in immunoglobulin loci, and solving the three-decade-old puzzle of explaining the mechanism for generating biomedically important, ultralong antibodies via tandem D-D fusions. She will also describe several collaborative projects in the emerging fields of personalized immunogenomics-analyzing how mutations in immunoglobulin loci affect our ability to develop antibodies that neutralize flu and HIV-and agricultural immunogenomics-analyzing cow antibody repertoires to assist in breeding efforts.
Yana Safonova received her BSc (2010) and MSc (2012) in computer science from the National Research State University of Nizhny Novgorod in Russia and her PhD (2017) in bioinformatics from Saint Petersburg State University. Since 2017, Safonova has been a postdoctoral scholar in the Computer Science and Engineering Department at the University of California, San Diego (UCSD). Since 2019, she has also been affiliated with the Department of Biochemistry and Molecular Genetics at the University of Louisville School of Medicine. Safonova's research interests cover open problems in immunogenomics and computational immunology that include applications of the recent repertoire sequencing technologies to the design of antibody drugs, the prediction of vaccine efficacy, and population analysis of immunity loci. Safonova was selected as a recipient of the Data Science Postdoctoral Fellowship (2017) by UCSD and the Intersect Fellowship for Computational Scientists and Immunologists (2019) by the American Associations of Immunologists. She is a member of the the Adaptive Immune Receptor Repertoire Community of the Antibody Society and is an author of a graduate-level immunogenomics course.