Zhang, Nancy RYakir, BenjaminXia, Charlie LSiegmund, David O2023-05-232023-05-232016-01-012017-08-21https://repository.upenn.edu/handle/20.500.14332/48080The 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 such scans, and derive formulas for false positive rate control and power calculations. The framework can also accommodate modified processes that involve overdispersion. 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.The original and published work is available at: https://projecteuclid.org/euclid.aoas/1469199891#abstractScan statisticsPoisson processeschange-point detectionnext-generation sequencingstructural variationPhysical Sciences and MathematicsArticle