2014/06/12 by Nancy R. Zhang, Zhang, Nancy R., Benjamin Yakir +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Genomics and Phylogenetic Studies #Methodology (stat.ME) #RNA and protein synthesis mechanisms #q-bio.GN #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.1406.3258
arxiv created 2014/06/12 · openalex publication_date 2014/06/12 · arxiv updated 2014/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.