DocumentCode :
2924102
Title :
LocalMotif - An In-Silico Tool for Detecting Localized Motifs in Regulatory Sequences
Author :
Narang, Vipin ; Sung, Wing-Kin ; Mittal, Ankush
Author_Institution :
Sch. of Comput., Singapore Nat. Univ.
fYear :
2006
fDate :
Nov. 2006
Firstpage :
791
Lastpage :
799
Abstract :
In silico motif finding algorithms are often used for discovering protein-DNA binding sites in a set of regulatory sequences. Current algorithms mainly address motif discovery in short sequences. Analyzing long sequences can be quite challenging not only due to increasing time and memory requirements of the algorithm, but also decreasing accuracy. However, in case the motif is localized in a short interval of the long sequences relative to an anchor point, it is tenable to detect it easily by restricting the search to that interval. But the region of localization of the motif is not known a priori. This paper reports an algorithm called LocalMotif to detect localized motifs in long regulatory sequences. A novel score function predicts the region of localization of the motif. This score is combined with other scoring measures including Z-score and relative entropy to detect the motif. The algorithm is optimized for fast processing of long regulatory sequences. Tests on simulated and real datasets confirm that LocalMotif accurately determines the region of localization of motifs and automatically discovers the biologically relevant motifs, which can be detected by other motif finding algorithms only when the search is restricted to the relevant interval
Keywords :
DNA; biology computing; genetic algorithms; genetics; LocalMotif; Z-score; biologically relevant motifs; localized motif detection; motif finding algorithms; motif localization region; protein-DNA binding; regulatory sequences; relative entropy; Algorithm design and analysis; Automatic testing; Biological system modeling; Biology computing; DNA; Drives; Entropy; Genomics; Protein engineering; Sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2006. ICTAI '06. 18th IEEE International Conference on
Conference_Location :
Arlington, VA
ISSN :
1082-3409
Print_ISBN :
0-7695-2728-0
Type :
conf
DOI :
10.1109/ICTAI.2006.76
Filename :
4031974
Link To Document :
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