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Beyond Enhancer-Promoter Contact: Le...

Rajpurkar, Aparna Rajiv.

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Beyond Enhancer-Promoter Contact: Leveraging Deep Learning to Connect Super-Resolution DNA Traces to Transcription.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Beyond Enhancer-Promoter Contact: Leveraging Deep Learning to Connect Super-Resolution DNA Traces to Transcription./
作者:	Rajpurkar, Aparna Rajiv.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	127 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
標題:	Genetics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28392140
ISBN:	9798569985166

Beyond Enhancer-Promoter Contact: Leveraging Deep Learning to Connect Super-Resolution DNA Traces to Transcription.
Rajpurkar, Aparna Rajiv.

Beyond Enhancer-Promoter Contact: Leveraging Deep Learning to Connect Super-Resolution DNA Traces to Transcription. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 127 p.

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

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

Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these complex datasets with a computationally unbiased method has not been achieved. In this dissertation, I present a deep learning-based approach to better understand to what degree chromatin structure relates to the transcriptional state of individual cells. Furthermore, I explore methods to "unpack the black box" to determine in an unbiased manner which structural features of chromatin regulation are most important for gene expression state. I apply this approach to the Optical Reconstruction of Chromatin Architecture dataset of the Bithorax gene cluster in Drosophila and show it significantly outperforms previous contact-focused methods. This work finds the structural information is distributed across the domain, overlapping and extending beyond domains identified by prior genetic analyses. Individual enhancer-promoter interactions are a minor contributor to predictions of activity.

ISBN: 9798569985166Subjects--Topical Terms:

530508
Genetics.
Subjects--Index Terms:

Chromatin architecture

Beyond Enhancer-Promoter Contact: Leveraging Deep Learning to Connect Super-Resolution DNA Traces to Transcription.
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500 $a Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
500 $a Advisor: Boettiger, Alistair;Bhatt, Ami;Kundaje, Anshul;Winslow, Monte.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these complex datasets with a computationally unbiased method has not been achieved. In this dissertation, I present a deep learning-based approach to better understand to what degree chromatin structure relates to the transcriptional state of individual cells. Furthermore, I explore methods to "unpack the black box" to determine in an unbiased manner which structural features of chromatin regulation are most important for gene expression state. I apply this approach to the Optical Reconstruction of Chromatin Architecture dataset of the Bithorax gene cluster in Drosophila and show it significantly outperforms previous contact-focused methods. This work finds the structural information is distributed across the domain, overlapping and extending beyond domains identified by prior genetic analyses. Individual enhancer-promoter interactions are a minor contributor to predictions of activity.
590 $a School code: 0212.
650 4 $a Genetics. $3 530508
650 4 $a Molecular chemistry. $3 1071612
653 $a Chromatin architecture
653 $a DNA
653 $a Single cells
653 $a Drosophila
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690 $a 0369
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 82-08B.
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