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Robust analysis of small RNA data an...

McCormick, Kevin P.

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Robust analysis of small RNA data and novel analysis techniques for discovery and validation of microRNAs and their targets.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Robust analysis of small RNA data and novel analysis techniques for discovery and validation of microRNAs and their targets./
作者:	McCormick, Kevin P.
面頁冊數:	136 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3598711
ISBN:	9781303475016

Robust analysis of small RNA data and novel analysis techniques for discovery and validation of microRNAs and their targets.
McCormick, Kevin P.

Robust analysis of small RNA data and novel analysis techniques for discovery and validation of microRNAs and their targets. - 136 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--University of Delaware, 2013.

Bioinformatics is becoming a popular approach to address biological questions. Next-generation sequencing has become a common means of measuring RNA transcripts. One important class of transcripts in eukaryotes are small RNAs. Because of their short length, small RNAs are easily assayed by next-generation sequencing, generating very large quantities of small RNA data.

ISBN: 9781303475016Subjects--Topical Terms:

626642
Computer Science.

Robust analysis of small RNA data and novel analysis techniques for discovery and validation of microRNAs and their targets.
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245 1 0 $a Robust analysis of small RNA data and novel analysis techniques for discovery and validation of microRNAs and their targets.
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500 $a Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
500 $a Advisers: Li Liao; Blake C. Meyers.
502 $a Thesis (Ph.D.)--University of Delaware, 2013.
520 $a Bioinformatics is becoming a popular approach to address biological questions. Next-generation sequencing has become a common means of measuring RNA transcripts. One important class of transcripts in eukaryotes are small RNAs. Because of their short length, small RNAs are easily assayed by next-generation sequencing, generating very large quantities of small RNA data.
520 $a The unique properties of small RNAs combined with the rich datasets available require specialized data handling and computational tools for analysis. Many small RNAs map to multiple genomic loci, and standard analysis techniques do not account for the fact that one locus may be more transcriptionally active than another. Most computational approaches to prediction of plant microRNA targets focus on sequence complementarity. Recent findings have shown many targets are missed by sequence-based approaches, and suggest that expression-based approaches may assist in the identification of novel targets.
520 $a In this work I develop a user-friendly computational pipeline for converting raw sequencing data into databases appropriate for biological analysis. I create an iterative version of an existing multi-read allocation technique, and develop a novel approach to address issues related to small RNA reads in particular with significantly reduced algorithmic complexity. Applying these techniques to microRNA detection, an improvement of as much as 16\% is achieved. I develop a novel method to predict microRNA targets on expression profiles, which, when combined with sequenced-based methods, improves the correlation of prediction scores with validation scores from 0.420 to 0.446.
590 $a School code: 0060.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Bioinformatics. $3 1018415
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710 2 $a University of Delaware. $b Department of Computer and Information Sciences. $3 1059001
773 0 $t Dissertation Abstracts International $g 75-02B(E).
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793 $a English
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