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Transcription factor binding site identification using the Self-Organizing Map

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https://ir.library.oregonstate.edu/concern/articles/mg74qm76j

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  • MOTIVATION: The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. Many existing approaches to motif identification do not always find the relevant biological motifs, or find only a subset of the occurrences of a motif. In this paper, we propose a self-organizing map of position weight matrices as an alternative method for motif discovery. The advantage of this approach is that it can be used to simultaneously characterize every feature present in the data set, thus lessening the chance that weaker signals will be missed. Features identified are ranked in terms of over-representation relative to a background model. RESULTS: We present an implementation of this approach, named SOMBRERO, which is capable of discovering multiple distinct motifs present in a single data set. Demonstrated here are the advantages of our approach on various data sets and SOMBRERO’s improved performance over two popular motif-finding programs; MEME and AlignACE. AVAILABILITY: SOMBRERO is available free of charge from http://bioinf.nuigalway.ie/sombrero.
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  • Mahony, S., Hendrix, D., Golden, A., Smith, T. J., & Rokhsar, D. S. (2005). Transcription factor binding site identification using the self-organizing map. Bioinformatics, 21(9), 1807-1814. doi:10.1093/bioinformatics/bti256
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  • 21
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  • 9
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  • S.M. thanks the Irish Research Council for Science, Engineering and Technology, and the NUI, Galway - University of California EAP program for supporting this work.
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