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Published online 26 February 2004

Nucleic Acids Research, 2004, Vol. 32, No. 4 1372-1381
© 2004 Oxford University Press

Detection of functional DNA motifs via statistical over-representation

Martin C. Frith1, Yutao Fu1, Liqun Yu2, Jiang-Fan Chen2, Ulla Hansen1,3 and Zhiping Weng*,1,4

1 Bioinformatics Program, Boston University, 44 Cummington Street, Boston, MA 02215, USA, 2 Department of Neurology, Boston University, 715 Albany Street, Boston, MA 02118, USA, 3 Department of Biology, Boston University, 5 Cummington Street, Boston, MA 02215, USA and 4 Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA

*To whom correspondence should be addressed. Tel: +1 617 353 3509; Fax: +1 617 353 6766; Email: zhiping{at}bu.edu

The interaction of proteins with DNA recognition motifs regulates a number of fundamental biological processes, including transcription. To understand these processes, we need to know which motifs are present in a sequence and which factors bind to them. We describe a method to screen a set of DNA sequences against a precompiled library of motifs, and assess which, if any, of the motifs are statistically over- or under-represented in the sequences. Over-represented motifs are good candidates for playing a functional role in the sequences, while under-representation hints that if the motif were present, it would have a harmful dysregulatory effect. We apply our method (implemented as a computer program called Clover) to dopamine-responsive promoters, sequences flanking binding sites for the transcription factor LSF, sequences that direct transcription in muscle and liver, and Drosophila segmentation enhancers. In each case Clover successfully detects motifs known to function in the sequences, and intriguing and testable hypotheses are made concerning additional motifs. Clover compares favorably with an ab initio motif discovery algorithm based on sequence alignment, when the motif library includes only a homolog of the factor that actually regulates the sequences. It also demonstrates superior performance over two contingency table based over-representation methods. In conclusion, Clover has the potential to greatly accelerate characterization of signals that regulate transcription.


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