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Published online by Cambridge University Press: 23 November 2009
Abstract
Motif discovery, which focuses on locating short sequence patterns associated with the regulation of genes in a species, leads to a class of statistical missing data problems. These problems are discussed first with reference to a hypothetical model, which serves as a point of departure for more realistic versions of the model. Some general results relating to modeling and inference through the Bayesian and/or frequentist perspectives are presented, and specific problems arising out of the underlying biology are discussed.
Introduction
The goal of motif discovery is to locate short repetitive patterns in DNA that are involved in the regulation of genes of interest. To fix ideas, let us consider the following paragraph modified from Bellhouse [4, Section 3, p. 5]:
Richard Bayes (1596–1675), a great-grandfather of Thomas Bayes, was a successful cutler in Sheffield. In 1643 Richard served in the rotating position of Master of the Company of Cutlers of Hallamshire. Richard was sufficiently well off that he sent one of his sons, Samuel Bayes (1635–1681) to Trinity College Cambridge during the Commonwealth period; Samuel obtained his degree in 1656. Another son, Joshua Bayes (1638–1703) followed in his father's footsteps in the cutlery industry, also serving as Master of the Company in 1679. Evidence of Joshua Bayes's wealth comes from the size of his house, the fact that he employed a servant and the size of the taxes that he paid. Joshua Bayes's influence may be taken from his activities in … Imagine that a person who has never seen the English language before looks at this paragraph and tries to make sense out of it.
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