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Large-scale phylogenetic reconstruct...

Tang, Jijun.

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Large-scale phylogenetic reconstruction from arbitrary gene-order data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large-scale phylogenetic reconstruction from arbitrary gene-order data./
作者:	Tang, Jijun.
面頁冊數:	115 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4682.
Contained By:	Dissertation Abstracts International65-09B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3148072
ISBN:	0496071645

Large-scale phylogenetic reconstruction from arbitrary gene-order data.
Tang, Jijun.

Large-scale phylogenetic reconstruction from arbitrary gene-order data. - 115 p.

Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4682.

Thesis (Ph.D.)--The University of New Mexico, 2004.

Phylogenetic reconstruction from gene-order data has attracted increasing attention from both biologists and computer scientists over the last few years. So far, our software suite GRAPPA is the most accurate approach. However, the approach---to evaluate every tree---is fundamentally slow. In 2001, we had to use a 512-processor cluster to analyze the 13-genome Campanulaceae dataset; our current version is one billion times faster than the original and can solve the same dataset on a laptop in less than an hour, but remains limited to at most 16 genomes.

ISBN: 0496071645Subjects--Topical Terms:

626642
Computer Science.

Large-scale phylogenetic reconstruction from arbitrary gene-order data.
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500 $a Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4682.
500 $a Adviser: Bernard M. E. Moret.
502 $a Thesis (Ph.D.)--The University of New Mexico, 2004.
520 $a Phylogenetic reconstruction from gene-order data has attracted increasing attention from both biologists and computer scientists over the last few years. So far, our software suite GRAPPA is the most accurate approach. However, the approach---to evaluate every tree---is fundamentally slow. In 2001, we had to use a 512-processor cluster to analyze the 13-genome Campanulaceae dataset; our current version is one billion times faster than the original and can solve the same dataset on a laptop in less than an hour, but remains limited to at most 16 genomes.
520 $a There are three main contributions in my dissertation. First, we used various algorithmic techniques to speedup GRAPPA, including a tightened lower bound, a layered search, and a branch-and-bound method, overall, these techniques made GRAPPA 1-billion times faster than its origin.
520 $a Although the speedup factor is significant, GRAPPA still cannot analyze a dataset with more than 15 genomes. We successfully scale up GRAPPA to hundreds of genomes by integrating GRAPPA with DCM, the disk-covering method pioneered by Tandy Warnow. DCM-GRAPPA can handle datasets with more than 1000 genomes and still retain high accuracy.
520 $a Finally, GRAPPA, like all existing reconstruction methods, requires that all genomes have identical gene content, with each gene appearing exactly once in each genome, which is highly unrealistic. We developed a collection of techniques to handle unequal gene contents, along with early experimental results showing that the ability to handle unequal contents makes a very significant difference in the accuracy of reconstructions.
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