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Computational methods for predicting...

Osada, Robert Radoslaw Zygmunt.

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Computational methods for predicting transcription factor binding sites.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational methods for predicting transcription factor binding sites./
Author:	Osada, Robert Radoslaw Zygmunt.
Description:	90 p.
Notes:	Adviser: Mona Singh.
Contained By:	Dissertation Abstracts International67-07B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3227347
ISBN:	9780542789878

Computational methods for predicting transcription factor binding sites.
Osada, Robert Radoslaw Zygmunt.

Computational methods for predicting transcription factor binding sites. - 90 p.

Adviser: Mona Singh.

Thesis (Ph.D.)--Princeton University, 2006.

A major challenge in computational biology is to understand the mechanisms that control gene expression. Transcription factor proteins mediate this process by interacting with a cell's DNA. Here the problem of identifying sequence-specific DNA binding sites of transcription factors is studied, taking two complementary approaches, one based primarily on identifying sequence features and the other exploiting a transcription factor's structure.

ISBN: 9780542789878Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Computational methods for predicting transcription factor binding sites.
LDR:02958nam 2200301 a 45 001 954097
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020 $a 9780542789878
035 $a (UMI)AAI3227347
035 $a AAI3227347
040 $a UMI $c UMI
100 1 $a Osada, Robert Radoslaw Zygmunt. $3 1277575
245 1 0 $a Computational methods for predicting transcription factor binding sites.
300 $a 90 p.
500 $a Adviser: Mona Singh.
500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3911.
502 $a Thesis (Ph.D.)--Princeton University, 2006.
520 $a A major challenge in computational biology is to understand the mechanisms that control gene expression. Transcription factor proteins mediate this process by interacting with a cell's DNA. Here the problem of identifying sequence-specific DNA binding sites of transcription factors is studied, taking two complementary approaches, one based primarily on identifying sequence features and the other exploiting a transcription factor's structure.
520 $a The first approach considers the problem of developing a representation for DNA binding sites known to be bound by a particular transcription factor, in order to recognize its other binding sites. The effectiveness of several commonly used approaches is compared, including position-specific scoring matrices, consensus sequences and match-mismatch based methods, showing that there are statistically significant differences in their performances. Furthermore, the use of per-position information content improves all basic approaches, and including local pairwise nucleotide dependencies within binding site models results in statistically significant improvements for approaches based on nucleotide matches. Based on the analysis, the best results when searching for DNA binding sites of a transcription factor are obtained by methods that use both information content and local pairwise correlations.
520 $a The second approach focuses on a particular structural class of transcription factors, the C2H2 zinc fingers, that comprise the largest family of eukaryotic transcription factors. Zinc finger protein-DNA interactions are modeled by their pairwise residue-base interactions that make up their structural interface using a modified support vector machine framework to find the favorability of each residue-base interaction. Unlike previous approaches, this framework includes not only examples of known interactions but also quantitative information about the relative binding affinities between different protein-DNA configurations. The resulting classifier performs well in a variety of cross-validation testing.
590 $a School code: 0181.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a Princeton University. $3 645579
773 0 $t Dissertation Abstracts International $g 67-07B.
790 $a 0181
790 1 0 $a Singh, Mona, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3227347