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Computational Discovery of Structure...

Brewer, Kenneth Ivan.

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Computational Discovery of Structured Non-Coding Rna Motifs in Bacteria.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Discovery of Structured Non-Coding Rna Motifs in Bacteria./
Author:	Brewer, Kenneth Ivan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	128 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321826
ISBN:	9798522996062

Computational Discovery of Structured Non-Coding Rna Motifs in Bacteria.
Brewer, Kenneth Ivan.

Computational Discovery of Structured Non-Coding Rna Motifs in Bacteria. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 128 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

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

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

This dissertation describes a range of computational efforts to discover novel structured non-coding RNA (ncRNA) motifs in bacteria and generate hypotheses regarding their potential functions. This includes an introductory description of key advances in comparative genomics and RNA structure prediction as well as some of the most commonly found ncRNA candidates. Beyond that, I describe efforts for the comprehensive discovery of ncRNA candidates in 25 bacterial genomes and a catalog of the various functions hypothesized for these new motifs. Finally, I describe the Discovery of Intergenic Motifs PipeLine (DIMPL) which is a new computational toolset that harnesses the power of support vector machine (SVM) classifiers to identify bacterial intergenic regions most likely to contain novel structured ncRNA and automates the bulk of the subsequent analysis steps required to predict function. In totality, the body of work will enable the large scale discovery of novel structured ncRNA motifs at a far greater pace than possible before.

ISBN: 9798522996062Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Computational discovery

Computational Discovery of Structured Non-Coding Rna Motifs in Bacteria.
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245 1 0 $a Computational Discovery of Structured Non-Coding Rna Motifs in Bacteria.
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300 $a 128 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Breaker, Ronald R.
502 $a Thesis (Ph.D.)--Yale University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation describes a range of computational efforts to discover novel structured non-coding RNA (ncRNA) motifs in bacteria and generate hypotheses regarding their potential functions. This includes an introductory description of key advances in comparative genomics and RNA structure prediction as well as some of the most commonly found ncRNA candidates. Beyond that, I describe efforts for the comprehensive discovery of ncRNA candidates in 25 bacterial genomes and a catalog of the various functions hypothesized for these new motifs. Finally, I describe the Discovery of Intergenic Motifs PipeLine (DIMPL) which is a new computational toolset that harnesses the power of support vector machine (SVM) classifiers to identify bacterial intergenic regions most likely to contain novel structured ncRNA and automates the bulk of the subsequent analysis steps required to predict function. In totality, the body of work will enable the large scale discovery of novel structured ncRNA motifs at a far greater pace than possible before.
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653 $a Comparative genomics
653 $a Intergenic regions
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