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|Title:||LocalMotif - An in-silico tool for detecting localized motifs in regulatory sequences|
|Source:||Narang, V.,Sung, W.-K.,Mittal, A. (2006). LocalMotif - An in-silico tool for detecting localized motifs in regulatory sequences. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI : 791-799. ScholarBank@NUS Repository. https://doi.org/10.1109/ICTAI.2006.76|
|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. © 2006 IEEE.|
|Source Title:||Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI|
|Appears in Collections:||Staff Publications|
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