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Open Access Publications from the University of California

Computational methods for the analysis of high throughput genomic data in cancer and development

  • Author(s): Pankov, Aleksandr
  • Advisor(s): Costello, Joseph
  • et al.
Abstract

This dissertation describes the research carried out within the scope of two projects that deal with novel biomedical technologies and their use for advancing medical knowledge through statistical integration of genomics assays. I have explored and characterized epigenetic intratumoral heterogeneity and brain cancer evolution by creating customized statistical analyses and novel methodology for understanding and integrating RNA- seq, methylation arrays, and exome-seq data. To explore functional effects of the Ilf2 RNA-binding protein (RBP) through embryonic stem cell differentiation in mice, I created statistical pipelines to remove data- generation artifacts, applied various testing methods, and integrated the information utilizing small RNA-seq, ribosome profiling, and RNA-seq technologies.

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