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 detection of gene regu...

Frith, Martin Cornelius.

Linked to FindBook

Google Book

Amazon

博客來

Computational detection of gene regulatory signals in nucleotide sequences.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational detection of gene regulatory signals in nucleotide sequences./
Author:	Frith, Martin Cornelius.
Description:	218 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3610.
Contained By:	Dissertation Abstracts International64-08B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3101074

Computational detection of gene regulatory signals in nucleotide sequences.
Frith, Martin Cornelius.

Computational detection of gene regulatory signals in nucleotide sequences. - 218 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3610.

Thesis (Ph.D.)--Boston University, 2004.

Genomes are the most ancient and profound of texts, containing the information necessary to build living creatures, including ourselves. This thesis describes computational tools developed for deciphering genomic regulatory signals, focusing primarily on regulation of gene transcription, and secondarily on localization of messenger RNA.Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Computational detection of gene regulatory signals in nucleotide sequences.
LDR:03028nmm 2200301 4500 001 1864940
005 20041216133903.5
008 130614s2004 eng d
035 $a (UnM)AAI3101074
035 $a AAI3101074
040 $a UnM $c UnM
100 1 $a Frith, Martin Cornelius. $3 1952405
245 1 0 $a Computational detection of gene regulatory signals in nucleotide sequences.
300 $a 218 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3610.
500 $a Major Professor: Zhiping Weng.
502 $a Thesis (Ph.D.)--Boston University, 2004.
520 $a Genomes are the most ancient and profound of texts, containing the information necessary to build living creatures, including ourselves. This thesis describes computational tools developed for deciphering genomic regulatory signals, focusing primarily on regulation of gene transcription, and secondarily on localization of messenger RNA.
520 $a Regulatory signals can be uncovered by gathering nucleotide sequences that share some biological property of interest, and identifying subsequence motifs common to them. Two approaches to discovering motifs in this way are described: an a priori method that searches for an optimal alignment of subsequences, and a technique that compares the sequences to a library of previously identified motifs and assesses which if any of the motifs are statistically overrepresented in the sequences.
520 $a Transcription is typically regulated by clusters of signals that are individually weak but collectively strong. Three statistical methods for identifying clusters of predefined motifs in large genomic sequences are developed, which consider both the quality of the motif matches and the tightness of their clustering. In addition, an algorithm for discovering unknown signals that occur multiple times in repeat clusters in one sequence is described.
520 $a These methods successfully identify signals previously known to regulate transcription and localization of messenger RNA, and also predict novel regulatory elements, some of which have since been confirmed experimentally. Finally, the challenge of reading the regulatory information in the mammalian genome is met in a concerted fashion: several types of promoter sequence are analyzed for statistically overrepresented motifs, and these motifs are then fed into a cluster-finding algorithm to detect other instances of these promoter types in the genome. This thesis demonstrates that computational methods can greatly accelerate the rate of discovery of regulatory elements in nucleotide sequences. All of the methods described herein have been carefully packaged and placed on the World Wide Web so that they are available to all researchers.
590 $a School code: 0017.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
690 $a 0308
690 $a 0369
710 2 0 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 64-08B.
790 1 0 $a Weng, Zhiping, $e advisor
790 $a 0017
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3101074