  As high-throughput sequencing has become common practice, the cost of
sequencing large amounts of genetic data has been drastically reduced, leading
to much larger data sets for analysis. One important task is to identify
biological conditions that lead to unusually high or low expression of a
particular gene. Packages such as DESeq implement a simple method for testing
differential signal when exactly two biological conditions are possible. For
more than two conditions, pairwise testing is typically used. Here the DESeq
method is extended so that three or more biological conditions can be assessed
simultaneously. Because the computation time grows exponentially in the number
of conditions, a Monte Carlo approach provides a fast way to approximate the
$p$-values for the new test. The approach is studied on both simulated data and
a data set of {\em C. jejuni}, the bacteria responsible for most food poisoning
in the United States.
