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Computational identification of euka...

Liu, Yueyi Irene.

Computational identification of eukaryotic regulatory elements and its application in Caenorhabditis elegans.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational identification of eukaryotic regulatory elements and its application in Caenorhabditis elegans.
作者:	Liu, Yueyi Irene.
面頁冊數:	101 p.
附註:	Adviser: Serafim Batzoglou.
附註:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4090.
Contained By:	Dissertation Abstracts International66-08B.
標題:	Biology, Molecular.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3187313
ISBN:	9780542295638

Computational identification of eukaryotic regulatory elements and its application in Caenorhabditis elegans.
Liu, Yueyi Irene.

Computational identification of eukaryotic regulatory elements and its application in Caenorhabditis elegans. - 101 p.

Adviser: Serafim Batzoglou.

Thesis (Ph.D.)--Stanford University, 2005.

ArrayScan is a program that predicts the experimental conditions under which regulatory motifs direct transcription by linear regression between the sequence motif scores of genes with the motif and their expression values. Using well-studied motifs, we show that ArrayScan can successfully identify microarray experiments under which these motifs are actively directing the transcription of their downstream genes. It also provided concrete hypotheses for a novel motif in yeast that can be tested in the laboratory.

ISBN: 9780542295638Subjects--Topical Terms:

226919
Biology, Molecular.

Computational identification of eukaryotic regulatory elements and its application in Caenorhabditis elegans.
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245 1 0 $a Computational identification of eukaryotic regulatory elements and its application in Caenorhabditis elegans.
300 $a 101 p.
500 $a Adviser: Serafim Batzoglou.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4090.
502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 # $a ArrayScan is a program that predicts the experimental conditions under which regulatory motifs direct transcription by linear regression between the sequence motif scores of genes with the motif and their expression values. Using well-studied motifs, we show that ArrayScan can successfully identify microarray experiments under which these motifs are actively directing the transcription of their downstream genes. It also provided concrete hypotheses for a novel motif in yeast that can be tested in the laboratory.
520 # $a CompareProspector is based on our systematic analyses of known regulatory elements that indicate these elements are more conserved than background sequences. It extends Gibbs sampling by biasing the search in promoter regions conserved across species. It successfully identified known motifs from human and C. elegans using human-mouse and C. elegans-Caenorhabditis briggsae comparison, respectively. It outperformed many other computational regulatory motif-finding programs on both human and C. elegans promoter sequences.
520 # $a I have also conducted a genome-wide search of regulatory motifs for the model organism C. elegans. I assembled 44 sets of genes that are co-expressed either under specific conditions or in a compendium of 553 microarray experiments. From the promoter sequences of these co-expressed genes, I identified 233 distinct motif groups in 6,690 C. elegans genes. Both computational validation and previous literature provide strong support for the biological significance of these motifs. The vast majority of these regulatory elements were not known before. Our search is a first step towards building a regulatory network for C. elegans .
520 # $a Regulatory elements are short DNA sequences that regulate the level, timing, and location of gene expression. Identifying regulatory elements and their functions is crucial in our understanding of gene regulation and disease processes. Many computational methods have previously been developed to identify these elements. Though they have enjoyed some success in bacteria and lower eukaryotes such as yeast, motif-finding in higher eukaryotes remain a challenge. In this thesis I present two novel computational algorithms, CompareProspector and ArrayScan, to address the above mentioned challenges. I also present a genome-wide search of regulation motifs for the model organism Caenorhabditis elegans.
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791 $a Ph.D.
792 $a 2005
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