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

Modeling evolution of protein coding...

Pond, Sergei L. Kosakovsky.

FindBook

Google Book

Amazon

博客來

Modeling evolution of protein coding DNA sequences.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling evolution of protein coding DNA sequences./
作者:	Pond, Sergei L. Kosakovsky.
面頁冊數:	121 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2258.
Contained By:	Dissertation Abstracts International64-05B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3090006

Modeling evolution of protein coding DNA sequences.
Pond, Sergei L. Kosakovsky.

Modeling evolution of protein coding DNA sequences. - 121 p.

Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2258.

Thesis (Ph.D.)--The University of Arizona, 2003.

We develop a new class of computationally feasible stochastic models for statistical analysis of genetic sequence evolution and inference of properties of the underlying substitution processes in the context of maximum likelihood framework. Existing models for evolution of protein coding sequences allow site to site variation in non-synonymous substitution rates, but assume that the rate of synonymous substitutions is constant for all sites. New models provide a rigorous statistical framework for testing the hypothesis of synonymous rate constancy, and enable a host of data exploration and analysis tools. For several indicative data sets, the constancy assumption is shown to be violated, and some possible explanations are given. We also present an algorithm for improving efficiency of maximum likelihood evaluations, and discuss <italic> HyPhy</italic>—a user friendly and publicly distributed software implementation of our methods.Subjects--Topical Terms:

517247
Statistics.

Modeling evolution of protein coding DNA sequences.
LDR:01812nmm 2200277 4500 001 1858351
005 20040927073707.5
008 130614s2003 eng d
035 $a (UnM)AAI3090006
035 $a AAI3090006
040 $a UnM $c UnM
100 1 $a Pond, Sergei L. Kosakovsky. $3 1946044
245 1 0 $a Modeling evolution of protein coding DNA sequences.
300 $a 121 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2258.
500 $a Director: Joseph C. Watkins.
502 $a Thesis (Ph.D.)--The University of Arizona, 2003.
520 $a We develop a new class of computationally feasible stochastic models for statistical analysis of genetic sequence evolution and inference of properties of the underlying substitution processes in the context of maximum likelihood framework. Existing models for evolution of protein coding sequences allow site to site variation in non-synonymous substitution rates, but assume that the rate of synonymous substitutions is constant for all sites. New models provide a rigorous statistical framework for testing the hypothesis of synonymous rate constancy, and enable a host of data exploration and analysis tools. For several indicative data sets, the constancy assumption is shown to be violated, and some possible explanations are given. We also present an algorithm for improving efficiency of maximum likelihood evaluations, and discuss <italic> HyPhy</italic>—a user friendly and publicly distributed software implementation of our methods.
590 $a School code: 0009.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
690 $a 0463
690 $a 0308
690 $a 0369
710 2 0 $a The University of Arizona. $3 1017508
773 0 $t Dissertation Abstracts International $g 64-05B.
790 1 0 $a Watkins, Joseph C., $e advisor
790 $a 0009
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3090006