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Novel algorithms for motif discovery...

Balla, Sudha.

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Novel algorithms for motif discovery in bio-sequence datasets.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Novel algorithms for motif discovery in bio-sequence datasets./
Author:	Balla, Sudha.
Description:	102 p.
Notes:	Adviser: Sanguthevar Rajasekaran.
Contained By:	Dissertation Abstracts International68-05B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3265756
ISBN:	9780549043201

Novel algorithms for motif discovery in bio-sequence datasets.
Balla, Sudha.

Novel algorithms for motif discovery in bio-sequence datasets. - 102 p.

Adviser: Sanguthevar Rajasekaran.

Thesis (Ph.D.)--University of Connecticut, 2007.

A significant growth in the volume of bio-molecular sequence data (DNA, RNA and protein sequences) over the past decade calls for novel computational techniques to extract meaningful information from such data. Existing methods to extract such information predominantly consist of identifying patterns or motifs, for example, repeated substrings of bio-sequences, conserved substrings in a group of homologous protein sequences, or similar substrings in a set of DNA sequences. Identifying such motifs has applications in, to name a few, understanding gene function, human disease, and identifying potential therapeutic drug targets. Several variants of the motif discovery problem could be identified in the literature and numerous algorithms have been proposed for such variants. In this research work, we propose novel algorithms, significantly different from the techniques adopted so far by the existing algorithms, to address salient problems in the domain of molecular biology that require discovering motifs in a set of bio-sequences. The proposed algorithms employ basic sorting techniques and simple data structures such as arrays and linked lists, and have been shown to perform better in practice than many of the previously known algorithms, when applied to synthetic and real biological datasets.

ISBN: 9780549043201Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Novel algorithms for motif discovery in bio-sequence datasets.
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020 $a 9780549043201
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100 1 $a Balla, Sudha. $3 1267132
245 1 0 $a Novel algorithms for motif discovery in bio-sequence datasets.
300 $a 102 p.
500 $a Adviser: Sanguthevar Rajasekaran.
500 $a Source: Dissertation Abstracts International, Volume: 68-05, Section: B, page: 2740.
502 $a Thesis (Ph.D.)--University of Connecticut, 2007.
520 $a A significant growth in the volume of bio-molecular sequence data (DNA, RNA and protein sequences) over the past decade calls for novel computational techniques to extract meaningful information from such data. Existing methods to extract such information predominantly consist of identifying patterns or motifs, for example, repeated substrings of bio-sequences, conserved substrings in a group of homologous protein sequences, or similar substrings in a set of DNA sequences. Identifying such motifs has applications in, to name a few, understanding gene function, human disease, and identifying potential therapeutic drug targets. Several variants of the motif discovery problem could be identified in the literature and numerous algorithms have been proposed for such variants. In this research work, we propose novel algorithms, significantly different from the techniques adopted so far by the existing algorithms, to address salient problems in the domain of molecular biology that require discovering motifs in a set of bio-sequences. The proposed algorithms employ basic sorting techniques and simple data structures such as arrays and linked lists, and have been shown to perform better in practice than many of the previously known algorithms, when applied to synthetic and real biological datasets.
590 $a School code: 0056.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
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710 2 $a University of Connecticut. $3 1017435
773 0 $t Dissertation Abstracts International $g 68-05B.
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790 1 0 $a Rajasekaran, Sanguthevar, $e advisor
791 $a Ph.D.
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3265756