DME is a program that discovers transcription factor binding site motifs in nucleotide sequences. DME identifies motifs, represented as position weight matrices, that are overrepresented in one set of sequences relative to another set. The ability to directly optimize relative overrepresentation is a unique feature of DME, making DME an ideal tool for analyzing promoters of transcripts found to have differential expression in a particular context. The optimization procedure is based on an enumerative algorithm that is guaranteed to identify optimal motifs from a discrete space of matrices with a specific lower bound on information content. This strategy scales very well with the number and length of the sequences used, and is well-suited to analyzing very large data sets.
DME2 is available for download as Open Source software licensed under GPL. [download source]
Here is the link to the development branch at Github.
References
Andrew D. Smith, Pavel Sumazin, and Michael Q. Zhang
Identifying tissue-selective transcription factor binding sites in vertebrate promoters.
Proc Natl Acad Sci. USA, 102(5):1560-1565, 2005.
Andrew D. Smith, Pavel Sumazin, Debopriya Das, and Michael Q. Zhang
Mining ChIP-chip data for transcription factor and cofactor binding sites.
Bioinformatics, 21(Suppl 1):i403-i412 (2005)
Andrew D. Smith, Pavel Sumazin, Zhenyu Xuan, and Michael Q. Zhang
DNA motifs in human and mouse proximal promoters predict tissue-specific expression.
Proc Natl Acad Sci. USA, 103(16):6275-6280, 2006.