Year: 2011
Type: dissertation
Abstract: In this thesis, I leveraged computational methods on biological data to better understand gene regulation and development of the human body, as well as of the model organisms mouse and yeast.Firstly, I tackled biological questions with machine learning techniques by studying pre-transcriptional gene regulation through nucleosome positioning, which resulted in the identification of function-specific factors and improved predictive performance.Next, computational analysis enabled the discovery of genome-wide epigenetic modifications that play a foundational role in silencing for the monoallelic and monogenic expression of olfactory receptor genes in mice.Lastly, signatures of functional, bound RNA regions provide insight into a potential protocol-specific bias and produce a new avenue for de novo discovery of functional regions.
Author: Yen, Angela
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W1550985177