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Modification Detection Using Nanopore Sequencing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modification Detection Using Nanopore Sequencing./
作者:	Bailey, Andrew D., IV.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	266 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28866114
ISBN:	9798780638964

Modification Detection Using Nanopore Sequencing.
Bailey, Andrew D., IV.

Modification Detection Using Nanopore Sequencing. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 266 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--University of California, Santa Cruz, 2021.

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

Both DNA and RNA modifications play critical roles in cell regulation. Traditionally, a chemical selection process alters base pairing or sequencing coverage is used to sequence modified nucleotides. Therefore, a new chemical labeling process needs to be created for each modification. Currently, we do not have methods for sequencing the majority of the over 150 RNA and over 40 DNA modifications. However, with nanopore sequencing, we can directly detect modifications on native DNA or RNA reads without any selection or chemical labeling techniques. Nanopore sequencing measures the change in current across a nanopore as a polynucleotide threads through the nanopore and we can use this signal to identify modifications. In chapter 1, we present a framework for the unsupervised determination of the number of nucleotide modifications from nanopore sequencing readouts. We demonstrate the approach can effectively recapitulate the number of modifications, the corresponding ionic current signal levels, as well as mixing proportions under both DNA and RNA contexts. We further show, by integrating information from multiple detected modification regions, that the modification status of DNA and RNA molecules can be inferred. This method forms a key step of de novo characterization of nucleotide modifications.In chapter 2, we present a graph convolutional network-based deep learning framework for predicting the mean of kmer distributions from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers.In chapter 3, using a combination of yeast genetics and nanopore direct RNA sequencing, we have developed a reliable method to track the modification status of single rRNA molecules at 37 sites in 18S rRNA and 73 sites in 25S rRNA. We use our method to characterize patterns of modification heterogeneity and identify concerted modification of nucleotides found near functional centers of the ribosome. Distinct undermodified subpopulations of rRNAs accumulate when ribosome biogenesis is compromised by loss of Dbp3 or Prp43-related RNA helicase function. Modification profiles are surprisingly resistant to change in response to many genetic and environmental conditions that affect translation, ribosome biogenesis, and pre-mRNA splicing. The ability to capture complete modification profiles for RNAs at single-molecule resolution will provide new insights into the roles of nucleotide modifications in RNA function.

ISBN: 9798780638964Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Hidden Markov model

Modification Detection Using Nanopore Sequencing.
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300 $a 266 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisor: Paten, Benedict.
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506 $a This item must not be sold to any third party vendors.
520 $a Both DNA and RNA modifications play critical roles in cell regulation. Traditionally, a chemical selection process alters base pairing or sequencing coverage is used to sequence modified nucleotides. Therefore, a new chemical labeling process needs to be created for each modification. Currently, we do not have methods for sequencing the majority of the over 150 RNA and over 40 DNA modifications. However, with nanopore sequencing, we can directly detect modifications on native DNA or RNA reads without any selection or chemical labeling techniques. Nanopore sequencing measures the change in current across a nanopore as a polynucleotide threads through the nanopore and we can use this signal to identify modifications. In chapter 1, we present a framework for the unsupervised determination of the number of nucleotide modifications from nanopore sequencing readouts. We demonstrate the approach can effectively recapitulate the number of modifications, the corresponding ionic current signal levels, as well as mixing proportions under both DNA and RNA contexts. We further show, by integrating information from multiple detected modification regions, that the modification status of DNA and RNA molecules can be inferred. This method forms a key step of de novo characterization of nucleotide modifications.In chapter 2, we present a graph convolutional network-based deep learning framework for predicting the mean of kmer distributions from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers.In chapter 3, using a combination of yeast genetics and nanopore direct RNA sequencing, we have developed a reliable method to track the modification status of single rRNA molecules at 37 sites in 18S rRNA and 73 sites in 25S rRNA. We use our method to characterize patterns of modification heterogeneity and identify concerted modification of nucleotides found near functional centers of the ribosome. Distinct undermodified subpopulations of rRNAs accumulate when ribosome biogenesis is compromised by loss of Dbp3 or Prp43-related RNA helicase function. Modification profiles are surprisingly resistant to change in response to many genetic and environmental conditions that affect translation, ribosome biogenesis, and pre-mRNA splicing. The ability to capture complete modification profiles for RNAs at single-molecule resolution will provide new insights into the roles of nucleotide modifications in RNA function.
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