東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Computational methods to advance phy...

Bazinet, Adam.

Linked to FindBook

Google Book

Amazon

博客來

Computational methods to advance phylogenomic workflows.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational methods to advance phylogenomic workflows./
Author:	Bazinet, Adam.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	325 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
Contained By:	Dissertation Abstracts International77-02B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725443
ISBN:	9781339097435

Computational methods to advance phylogenomic workflows.
Bazinet, Adam.

Computational methods to advance phylogenomic workflows. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 325 p.

Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2015.

This item is not available from ProQuest Dissertations & Theses.

Phylogenomics refers to the use of genome-scale data in phylogenetic analysis. There are several methods for acquiring genome-scale, phylogenetically-useful data from an organism that avoid sequencing the entire genome, thus reducing cost and effort, and enabling one to sequence many more individuals. In this dissertation we focus on one method in particular---RNA sequencing---and the concomitant use of assembled protein-coding transcripts in phylogeny reconstruction. Phylogenomic workflows involve tasks that are algorithmically and computationally demanding, in part due to the large amount of sequence data typically included in such analyses. This dissertation applies techniques from computer science to improve methodology and performance associated with phylogenomic workflow tasks such as sequence classification, transcript assembly, orthology determination, and phylogenetic analysis. While the majority of the methods developed in this dissertation can be applied to the analysis of diverse organismal groups, we primarily focus on the analysis of transcriptome data from Lepidoptera (moths and butterflies), generated as part of a collaboration known as "Leptree".

ISBN: 9781339097435Subjects--Topical Terms:

523869
Computer science.

Computational methods to advance phylogenomic workflows.
LDR:02188nmm a2200313 4500 001 2121132
005 20170724102950.5
008 180830s2015 ||||||||||||||||| ||eng d
020 $a 9781339097435
035 $a (MiAaPQ)AAI3725443
035 $a AAI3725443
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bazinet, Adam. $3 3283126
245 1 0 $a Computational methods to advance phylogenomic workflows.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 325 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
500 $a Adviser: Michael P. Cummings.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Phylogenomics refers to the use of genome-scale data in phylogenetic analysis. There are several methods for acquiring genome-scale, phylogenetically-useful data from an organism that avoid sequencing the entire genome, thus reducing cost and effort, and enabling one to sequence many more individuals. In this dissertation we focus on one method in particular---RNA sequencing---and the concomitant use of assembled protein-coding transcripts in phylogeny reconstruction. Phylogenomic workflows involve tasks that are algorithmically and computationally demanding, in part due to the large amount of sequence data typically included in such analyses. This dissertation applies techniques from computer science to improve methodology and performance associated with phylogenomic workflow tasks such as sequence classification, transcript assembly, orthology determination, and phylogenetic analysis. While the majority of the methods developed in this dissertation can be applied to the analysis of diverse organismal groups, we primarily focus on the analysis of transcriptome data from Lepidoptera (moths and butterflies), generated as part of a collaboration known as "Leptree".
590 $a School code: 0117.
650 4 $a Computer science. $3 523869
650 4 $a Systematic biology. $3 3173492
650 4 $a Bioinformatics. $3 553671
690 $a 0984
690 $a 0423
690 $a 0715
710 2 $a University of Maryland, College Park. $b Computer Science. $3 1018451
773 0 $t Dissertation Abstracts International $g 77-02B(E).
790 $a 0117
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725443