Authors:
S. Michelet
1
;
J-P. Barbot
1
;
O. Francais
1
;
P-Y. Joubert
1
;
P. Larzabal
1
;
R. Kawano
2
;
H. Sasaki
2
;
T. Osaki
2
;
S. Takeuchi
2
and
B. Le Pioufle
1
Affiliations:
1
CNRS and UniverSud, France
;
2
University of Tokyo, Japan
Keyword(s):
Signal processing, Stochastic framework, Detection, Estimation, Characterization of DNA translocations.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Telecommunications
Abstract:
This paper investigates the properties of DNA translocations signals in a stochastic framework. The considered signals are relative to the translocation of single strand DNA through natural nanopores, and are obtained using a planar patch clamp method. The stochastic signal analysis is carried out considering the statistical distribution of DNA translocation parameters, considered as random variables including the amplitude, the duration and the apparition of the DNA translocation events as well as the no-translocation signal features. For each of these variables, a distribution function is proposed and assessed using a Kolmogorov-Smirnov test, and their features are estimated. The DNA translocation signal stochastic analysis enables to characterize the detection and/or estimation performances of existing algorithms, such as a breakdown detection algorithm, in a stochastic framework. Moreover, it opens the way to the design of model based algorithms such as detection tests using a li
kelihood ratio or joint detection-estimation algorithms using a maximum likelihood approach, for an enhanced characterization of DNA translocations.
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