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Computational methods for finding re...

Boston University.

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Computational methods for finding regulatory DNA motifs using sequence characteristics and positional preferences.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational methods for finding regulatory DNA motifs using sequence characteristics and positional preferences./
作者:	Tharakaraman, Kannan.
面頁冊數:	191 p.
附註:	Adviser: John L. Spouge.
Contained By:	Dissertation Abstracts International69-05B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3314081
ISBN:	9780549635031

Computational methods for finding regulatory DNA motifs using sequence characteristics and positional preferences.
Tharakaraman, Kannan.

Computational methods for finding regulatory DNA motifs using sequence characteristics and positional preferences. - 191 p.

Adviser: John L. Spouge.

Thesis (Ph.D.)--Boston University, 2008.

Many biologically active regions of a genome can be discovered by searching for small sequence patterns, or "motifs." A class of motifs of great interest in biology corresponds to sites bound by gene regulatory proteins. The shortness and degeneracy of these sites have, however, frustrated standard sequence-based, motif discovery methods. Moreover, as classical experiments in Molecular Biology have shown, a binding site for a regulatory protein can assume different biological functions in different promoter regions, rendering standard methods unsuitable for motif classification.

ISBN: 9780549635031Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Computational methods for finding regulatory DNA motifs using sequence characteristics and positional preferences.
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245 1 0 $a Computational methods for finding regulatory DNA motifs using sequence characteristics and positional preferences.
300 $a 191 p.
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500 $a Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 2705.
502 $a Thesis (Ph.D.)--Boston University, 2008.
520 $a Many biologically active regions of a genome can be discovered by searching for small sequence patterns, or "motifs." A class of motifs of great interest in biology corresponds to sites bound by gene regulatory proteins. The shortness and degeneracy of these sites have, however, frustrated standard sequence-based, motif discovery methods. Moreover, as classical experiments in Molecular Biology have shown, a binding site for a regulatory protein can assume different biological functions in different promoter regions, rendering standard methods unsuitable for motif classification.
520 $a Previous studies have shown that the binding sites for some regulatory proteins have positional preferences with respect to the transcription start site. Making use of the precise transcription start site locations, this thesis describes computational methods to detect binding sites based on their positional and nucleotide preferences. Three different methods of this type are described: (1) an enumerative statistical test, related to gapless BLAST statistics, that detects octanucleotides that are unusually clustered with respect to the transcription start site in promoter sequences, (2) a Gibbs-sampler program that can use the results generated by the statistic (mentioned in 1) to anchor a multiple alignment on any set of positions thought to contribute to a common binding site, and (3) a statistical method to detect clusters of previously defined motifs in promoter sequences anchored on the transcription start site. Extensions to the Gibbs sampler program including a post-processing step, a Markov background model and a Bayesian positional model are also described.
520 $a Examples from datasets containing known binding sites revealed that positional information lends better retrieval accuracy. In silico validation of the motifs using gene expression data and functional similarity data demonstrated that some binding sites can have two different roles in transcription regulation (activation or repression), depending on where they are positioned with respect to the transcription start site. The results from this thesis broaden our understanding of positional control in gene regulation, and illustrate the significance of incorporating positional information in motif discovery methods. All the tools developed in this study have been made available for download via the World Wide Web.
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