  Large-scale statistical analysis of data sets associated with genome
sequences plays an important role in modern biology. A key component of such
statistical analyses is the computation of $p$-values and confidence bounds for
statistics defined on the genome. Currently such computation is commonly
achieved through ad hoc simulation measures. The method of randomization, which
is at the heart of these simulation procedures, can significantly affect the
resulting statistical conclusions. Most simulation schemes introduce a variety
of hidden assumptions regarding the nature of the randomness in the data,
resulting in a failure to capture biologically meaningful relationships. To
address the need for a method of assessing the significance of observations
within large scale genomic studies, where there often exists a complex
dependency structure between observations, we propose a unified solution built
upon a data subsampling approach. We propose a piecewise stationary model for
genome sequences and show that the subsampling approach gives correct answers
under this model. We illustrate the method on three simulation studies and two
real data examples.
