Journal of Data Science

Abstract: DNA fingerprinting is a microbiological technique widely used to find a DNA sequence specific for a microbe. It involves slicing the genomes of the microbe into DNA fragments with manageable sizes, sorting the DNA pieces by length and finally identifying a DNA sequence unique to the mi crobe, using probe-based assays. This unique DNA is referred to as DNA fingerprint of the microbe under study. In this paper, we introduce a proba bilistic model to estimate the chance of identifying the DNA fingerprint from the genome of a microbe when the DNA fingerprinting method is employed. We derive a closed-form functional relationship between the chance of find ing the fingerprint and factors that can be experimentally controlled either in part, fully or not at all. Because the odds of finding a specific DNA fin gerprint can only be improved by experimental design to a certain degree, in a broader sense, we show that the discovery of a DNA fingerprint is a process governed more by chance than by design. Nevertheless, the results can be potentially used to guide experiments in maximizing the chance of finding a DNA fingerprint of interest.

Abstract: Several statistical approaches have been proposed to consider circumstances under which one universal distribution is not capable of fit ting into the whole domain. This paper studies Bayesian detection of mul tiple interior epidemic/square waves in the interval domain, featured by two identical statistical distributions at both ends. We introduce a simple dimension-matching parameter proposal to implement the sampling-based posterior inference for special cases where each segmented distribution on a circle has the same set of regulating parameters. Molecular biology research reveals that, cancer progression may involve DNA copy number alteration at genome regions and connection of two biologically inactive chromosome ends results in a circle holding multiple epidemic/square waves. A slight modification of a simple novel Bayesian change point identification algo rithm, random grafting-pruning Markov chain Monte Carlo (RGPMCMC), is proposed by adjusting the original change point birth/death symmetric transition probability with a differ-by-one change point number ratio. The algorithm performance is studied through simulations with connection to DNA copy number alteration detection, which promises potential applica tion to cancer diagnosis at the genome level.