Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/btq106
Title: Localized motif discovery in gene regulatory sequences
Authors: Narang, V.
Mittal, A.
Sung, W.-K. 
Issue Date: 2010
Source: Narang, V., Mittal, A., Sung, W.-K. (2010). Localized motif discovery in gene regulatory sequences. Bioinformatics 26 (9) : 1152-1159. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/btq106
Abstract: Motivation: Discovery of nucleotide motifs that are localized with respect to a certain biological landmark is important in several applications, such as in regulatory sequences flanking the transcription start site, in the neighborhood of known transcription factor binding sites, and in transcription factor binding regions discovered by massively parallel sequencing (ChIP-Seq). Results: We report an algorithm called LocalMotif to discover such localized motifs. The algorithm is based on a novel scoring function, called spatial confinement score, which can determine the exact interval of localization of a motif. This score is combined with other existing scoring measures including over-representation and relative entropy to determine the overall prominence of the motif. The approach successfully discovers biologically relevant motifs and their intervals of localization in scenarios where the motifs cannot be discovered by general motif finding tools. It is especially useful for discovering multiple co-localized motifs in a set of regulatory sequences, such as those identified by ChIP-Seq. Availability and Implementation: The LocalMotif software is available at http://www.comp.nus.edu.sg/~bioinfo/LocalMotif. Contact: ksung@comp.nus.edu.sg. Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.
Source Title: Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/38896
ISSN: 13674803
DOI: 10.1093/bioinformatics/btq106
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