Hidden Markov model analysis to fluorescence blinking of fluorescently labeled DNA
Tatsuhiro Furuta, Shuya Fan, Tadao Takada, Yohei Kondo, Mamoru, Fujitsuka, Atsushi Maruyama, Kiyohiko Kawai, Kazuma Nakamura

TL;DR
This study applies hidden Markov models to analyze fluorescence blinking in labeled DNA, revealing exponential ON-duration and log-normal OFF-duration distributions, advancing quantitative understanding of molecular fluorescence dynamics.
Contribution
It introduces a hidden Markov model approach to quantitatively analyze fluorescence blinking, identifying specific probability distributions for ON and OFF states in DNA molecules.
Findings
ON-duration follows an exponential distribution
OFF-duration follows a log-normal distribution
Time-bin dependence affects fluorescence state probabilities
Abstract
We examine quantitatively the transition process from emitting to not-emitting states of fluorescent molecules with a machine learning technique. In a fluorescently labeled DNA, the fluorescence occurs continuously under irradiation, but it often transfers to the not-emitting state corresponding to a charge-separated state. The trajectory of the fluorescence consists of repetitions of light-emitting (ON) and not-emitting (OFF) states, called blinking, and it contains a very large amount of noise due to the several reasons, so in principle, it is difficult to distinguish the ON and OFF states quantitatively. The fluorescence trajectory is a typical stochastic process, and therefore requires advanced time-series data analysis. In the present study, we analyze the fluorescence trajectories using a hidden Markov model, and calculate the probability density of the ON and OFF duration. From…
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Taxonomy
TopicsRNA Interference and Gene Delivery · DNA and Nucleic Acid Chemistry
