東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Computational methods for improving ...

Powell, Bradford Cole.

FindBook

Google Book

Amazon

博客來

Computational methods for improving genome annotation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational methods for improving genome annotation./
作者:	Powell, Bradford Cole.
面頁冊數:	101 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0073.
Contained By:	Dissertation Abstracts International67-01B.
標題:	Biology, Genetics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200834
ISBN:	9780542485596

Computational methods for improving genome annotation.
Powell, Bradford Cole.

Computational methods for improving genome annotation. - 101 p.

Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0073.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2005.

The growing availability of genome sequence information has transformed biology. The analysis and annotation of this information is an ongoing process. Because such a large amount of information is involved, computers have been an important part of this process. This dissertation describes three studies in which computational methods are used to interpret different levels of annotation of data derived from genome sequences.

ISBN: 9780542485596Subjects--Topical Terms:

1017730
Biology, Genetics.

Computational methods for improving genome annotation.
LDR:03247nmm 2200313 4500 001 1822930
005 20061127103245.5
008 130610s2005 eng d
020 $a 9780542485596
035 $a (UnM)AAI3200834
035 $a AAI3200834
040 $a UnM $c UnM
100 1 $a Powell, Bradford Cole. $3 1912058
245 1 0 $a Computational methods for improving genome annotation.
300 $a 101 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0073.
500 $a Adviser: Clyde A. Hutchinson, III.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2005.
520 $a The growing availability of genome sequence information has transformed biology. The analysis and annotation of this information is an ongoing process. Because such a large amount of information is involved, computers have been an important part of this process. This dissertation describes three studies in which computational methods are used to interpret different levels of annotation of data derived from genome sequences.
520 $a In the first study, a graph-based clustering technique is applied to nucleotide sequence similarity data. Open reading frames from different organisms are grouped according to similarity using a modified analysis of Clusters of Orthologous Groups (COGS). Sequences which are preserved through multiple organisms may have a functional role, and in particular may encode proteins. Examining inconsistent gene predictions within these groups reveals target sequences that may be genes missed by current prediction methods. Simultaneous comparison among several genomes informs upon a fundamental level of genome annotation: where protein-coding genes are located.
520 $a The second study involves individual exons of Dscam. In insects, this gene is capable of generating enormous transcript diversity through alternative splicing. Iterated searches were used to find homologous exons, and the evolutionary history of these exons was studied using Bayesian phylogenetic inference. Our results indicate that the different alternatively spliced exon groups differ in their rates of fixed mutations and in the turnover in the variants available for alternative splicing. The functional role of insect Dscam and of its human orthologs remains unclear, but any explanation of this role must take into account the transcript diversity which is so prominent in insects but which has not been seen outside of this lineage.
520 $a The third study details several sources of variability in quantitation of genetic transcripts by real-time monitoring of the polymerase chain reaction. This study indicates that these measurements can be misleadingly precise-systematic bias introduced in the analysis may introduce more variability than is seen when examining a standard curve. Understanding the causes of this variability is important for interpreting annotations of gene transcript levels.
590 $a School code: 0153.
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Biology, Microbiology. $3 1017734
690 $a 0369
690 $a 0410
710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
773 0 $t Dissertation Abstracts International $g 67-01B.
790 1 0 $a Hutchinson, Clyde A., III, $e advisor
790 $a 0153
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200834