Negative information for motif discovery.
We discuss a method of combining genome-wide transcription factor binding data, gene expression data, and genome sequence data for the purpose of motif discovery in S. cerevisiae. Within the word-counting algorithmic approach to motif discovery, we present a method of incorporating information from negative intergenic regions where a transcription factor is thought not to bind, and a statistical significance measure which account for intergenic regions of different lengths. Our results demonstrate that our method performs slightly better than other motif discovery algorithms. Finally, we present significant potential new motifs discovered by the algorithm.
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. kenta@mit.edu
K T
KT
Takusagawa
D K
DK
Gifford
D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology; D016384:Consensus Sequence; D004271:DNA, Fungal / Q000235:genetics / Q000378:metabolism; D021901:DNA, Intergenic; D008957:Models, Genetic; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism*
D004271:DNA, Fungal; D021901:DNA, Intergenic; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors