Transcription factor binding site identification using the self-organizing map

Authors:
Shaun Mahony;David Hendrix;Aaron Golden;Terry J. Smith;Daniel S. Rokhsar
Affiliations:
National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland;Department of Physics, University of California Berkeley, CA 94720, USA;National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland;National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland;Center for Integrative Genomics, University of California Berkeley, CA 94720, USA
Venue:
Bioinformatics
Year:
2005

Citing 0
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Abstract

Motivation: The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. 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 dataset, 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 (self-organizing map for biological regulatory element recognition and ordering), which is capable of discovering multiple distinct motifs present in a single dataset. Demonstrated here are the advantages of our approach on various datasets 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 Contact: shaun.mahony@nuigalway.ie Supplementary information: http://bioinf.nuigalway.ie/sombrero/additional