MAY 11, 2017 06:00 AM PDT

Transcriptome Analysis; Tackling core issues related to regulation & also mining the "data exhaust" of this activity

C.E. CREDITS: CEU | P.A.C.E. CE | Florida CE
  • Albert L. Williams Professor of Biomedical Informatics, Co-Director, Yale Computational biology and Bioinformatics Program, Yale University

      Mark Gerstein is the Albert L Williams professor of Biomedical Informatics at Yale University. He is co-director the Yale Computational Biology and Bioinformatics Program, and has appointments in the Department of Molecular Biophysics and Biochemistry and the Department of Computer Science. He received his AB in physics summa cum laude from Harvard College and his PhD in chemistry from Cambridge. He did post-doctoral work at Stanford and took up his post at Yale in early 1997. Since then he has published appreciably in scientific journals. He has >400 publications in total, with a number of them in prominent journals, such as Science, Nature, and Scientific American. (His current publication list is at .) His research is focused on bioinformatics, and he is particularly interested in large-scale integrative surveys, biological database design, macromolecular geometry, molecular simulation, human genome annotation, gene expression analysis, and data mining. 


    In this seminar, I will discuss issues in transcriptome analysis. I will first talk about core aspects - how we analyze the activity patterns of genes in model organisms and humans. I will focus on how we cluster these patterns together, finding conserved modules across species, and then, how we analyze the regulation of these modules, whether their dynamics is determined internally or involves an external control. Finally, I will talk about how one can decompose this regulation into simple logic gates, such as those seen in electronic circuits (e.g., and/or), and how one finds a different type of gate in the natural functioning of cells than in the dis-regulated activity evident in cancer. In the second half of the talk, I will look at some of the data exhaust from transcriptome analysis. That is, how one can find additional things from this data than what is necessarily intended. I will focus on genomic privacy: How looking at the quantifications of expression levels can potentially reveal something about the subjects studied, and how one can take steps to protect patient anonymity.

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