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Download the Paper (270 K, PDF file) - May 3, 2002 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
This paper surveys and extends models and algorithms for identifying binding sites in non-coding regions of DNA. These sites control the transcription of genes into messenger RNA in preparation for translation into proteins. We summarize the underlying biology, review three different models for binding site identification, and present a unified model that borrows from the previous models and integrates their main features. We then describe maximum likelihood and maximum a posteriori algorithms for fitting the unified model to data. Finally, we conclude with a prospectus of future data analyses and theoretical research.

SUGGESTED CITATION:
Chiara Sabatti and Kenneth Lange, "Genomewide Motif Identification Using a Dictionary Model" (May 3, 2002). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2002050301.
http://repositories.cdlib.org/uclastat/papers/2002050301