  The detection of local genomic signals using high-throughput DNA sequencing
data can be cast as a problem of scanning a Poisson random field for local
changes in the rate of the process. We propose a likelihood-based framework for
for such scans, and derive formulas for false positive rate control and power
calculations. The framework can also accommodate mixtures of Poisson processes
to deal with over-dispersion. As a specific, detailed example, we consider the
detection of insertions and deletions by paired-end DNA-sequencing. We propose
several statistics for this problem, compare their power under current
experimental designs, and illustrate their application on an Illumina Platinum
Genomes data set.
