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Leveraging Functional Genomic Data: ...

Lee, Donghoon.

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Leveraging Functional Genomic Data: Modeling Transcriptional Dynamics and Interpreting Disease Genomes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Leveraging Functional Genomic Data: Modeling Transcriptional Dynamics and Interpreting Disease Genomes./
Author:	Lee, Donghoon.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	338 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22623741
ISBN:	9798607313593

Leveraging Functional Genomic Data: Modeling Transcriptional Dynamics and Interpreting Disease Genomes.
Lee, Donghoon.

Leveraging Functional Genomic Data: Modeling Transcriptional Dynamics and Interpreting Disease Genomes. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 338 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--Yale University, 2019.

This item must not be sold to any third party vendors.

Gene regulation is central to the biological function of an organism. It involves intricate processes which require precise spatio-temporal coordination of transcription factors and epigenetic context. However, there remain challenges in the modeling of gene regulation and interpreting the functional consequence of gene dysregulation in disease genomes. We have witnessed rapid growth in the volume of functional genomic data produced by emerging deep sequencing technology in the last decade. Together with the development of novel functional characterization technologies, the accumulation of deeply sequenced genomic data has brought new opportunities to gain better insights about how a gene is regulated. This dissertation aims to leverage the vast amount of functional genomic data to derive annotations useful for interpreting disease genomes. Specifically, I present mathematical models and computational frameworks that aim to decipher disease genomes by integrating layers of functional genomic data. I describe a new prioritization scheme to quantify dysregulation of transcription, from finding burdened genomic elements down to a single point mutation that could disrupt normal function. Furthermore, application to cancer shows the utility of the integrative model. It expands our knowledge of noncoding regulation and facilitates our interpretation of noncoding mutation effects and aberrant gene expression patterns in cancer. The resulting carefully calibrated whole genome somatic and germline mutations, transcriptional profiles, and phenotypic annotations provide a valuable resource for studying noncoding dysregulation in cancer. Altogether, this dissertation presents novel integrative methods that leverage functional genomic data to accurately model the transcriptional regulation and demonstrates the utility of the integrative methods in meeting the challenge of interpretation of diseased genomes.

ISBN: 9798607313593Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Cancer

Leveraging Functional Genomic Data: Modeling Transcriptional Dynamics and Interpreting Disease Genomes.
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