Poster Print Size: Nanopore Sensors And Signal Processing … › ... › 08 ›...
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Nanopore Sensors And Signal Processing Melvin L Bowers III ,REU Student, Shepherd University Graduate Mentor:Uday Shankar Shanthamallu Faculty Advisors: Dr. Michael Goryll, Dr. Andreas Spanias MOTIVATION Ion channels and nanopores can be modified and used to sequence DNA or detect specific disease relevant or toxic molecules [1-3]. PROBLEM STATEMENT Signals are small and signatures are difficult to distinguish from artifacts. System-related noise often masks transitions between different states of the ion channel or nanopore sensor. Noise filtering is performed in the analog domain, often distorting the original shape of the signal. Analog filtering can impact event classification, leading to false positives or negatives. METHOD Simulate ion channel signals using QuB to determine the expected signal behavior and analyze specific features in the signal. Use wavelet decomposition [4]. Reduce white noise by picking only the most prominent wavelet components for re-synthesis. Use the Signal-to-noise ratio (SNR) values to compare the efficiency of noise reduction. : FINAL RESULTS Imported files into MATLAB and utilized MATLAB’S library of wavelets to perform an analysis of the simulated signal. Utilizing different wavelets we determined the wavelet and coefficients that would result in the highest fidelity of the re-synthesized signal compared to the original. The optimum wavelet and order was quantified by computing the SNR value for every de-noised signal which was then subtracted from the original SNR. The SNR of the de- noised signal includes the complete signal, taking into account possible over- and undershoot events at the state transitions, which are considered detrimental to subsequent classification. Sensor Signal and Information Processing Center http://sensip.asu.edu SenSIP Algorithms and Devices REU ABSTRACT REFERENCES Ion channels are involved with everything from pain to disease in any organism. Signal processing can be applied to de-noise ion-channel generated signals. We demonstrate the use of Wavelet analysis and re- synthesis for de-noising, selecting the optimal wavelet base for the particular nature of ion-channel and nanopore signals. [1] J. Clarke, H. Bayley et al., Nature Nanotechnology 4, 265–270 (2009). [2] I. M. Derrington, E. Manrao, J. H. Gundlach et al., PNAS 107 (37), 16060-16065 (2010). [3] S. Choi, M. Goryll, L.Y.M. Sin,et al. Microfluid Nanofluid (2011) 10: 231. doi:10.1007/s10404-010-0638-8 [4] B. Konnanath, P. Sattigeri, T. Mathew, A. Spanias, S.Prasad, M. Goryll, T. J. Thornton , P. Knee, ICANN09 This material is based upon work supported by the National Science Foundation under Grant No. CNS 1659871 REU Site: Sensors, Signal and Information Processing Devices and Algorithms. Number of wavelet orders used Wavelet type SNR of original signal (dB) SNR of denoised signal (dB) SNR gained or lost from baseline (dB) 5 Haar 18.29 19.22 0.93 6 Sym 18.29 18.00 -0.29 5 Coif 18.29 21.02 2.73 5 Bior 18.29 19.22 0.93 5 Rbio 18.29 19.22 0.93 4 Dmey 18.29 23.72 5.43 4 Fk 18.29 21.88 3.59 Original Signal 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 Time (ms) -0.4 1.4 1.2 1 0.8 0.4 0 0.6 0.2 -0.2 Amplitude (a.u.) Denoised DMEY 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 Time (ms) 1 0.4 0 0.6 0.2 0.8 Amplitude (a.u.) Denoised HAAR 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 Time (ms) 0 0.8 0.7 1 0.6 0.4 0.5 0.3 0.1 Amplitude (a.u.) 0.2 0.9 Amplitude (a.u.) Denoised SYM 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 Time (ms) -0.2 1.2 1 0.8 0.4 0 0.6 0.2 Amplitude (a.u.) Denoised FK 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 Time (ms) 1 0.4 0 0.6 0.2 0.8 Amplitude (a.u.) Representative signals from biological nanopore sensors used for DNA sequencing: (a) using an a-HL pore [1] and (b) using an MspA pore [2]. Simulated ion channel signal with added white noise using the QuB software package. The red trace shows the idealized signal using a Hidden Markov Model for state classification. (https://qub.mandelics.com/online)
Transcript of Poster Print Size: Nanopore Sensors And Signal Processing … › ... › 08 ›...
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Nanopore Sensors And Signal Processing Melvin L Bowers III ,REU Student, Shepherd University
Graduate Mentor:Uday Shankar Shanthamallu Faculty Advisors: Dr. Michael Goryll, Dr. Andreas Spanias
MOTIVATION
Ion channels and nanopores can be modified and used to sequence DNA or detect specific disease relevant or toxic molecules [1-3].
PROBLEM STATEMENT
Signals are small and signatures are difficult to distinguish from artifacts.
System-related noise often masks transitions between different states of the ion channel or nanopore sensor.
Noise filtering is performed in the analog domain, often distorting the original shape of the signal.
Analog filtering can impact event classification, leading to false positives or negatives.
METHOD
Simulate ion channel signals using QuB to determine the expected signal behavior and analyze specific features in the signal.
Use wavelet decomposition [4].
Reduce white noise by picking only the most prominent wavelet components for re-synthesis.
Use the Signal-to-noise ratio (SNR) values to compare the efficiency of noise reduction.
:
FINAL RESULTS
Imported files into MATLAB and utilized MATLAB’S library of wavelets to perform an analysis of the simulated signal.
Utilizing different wavelets we determined the wavelet and coefficients that would result in the highest fidelity of the re-synthesized signal compared to the original.
The optimum wavelet and order was quantified by computing the SNR value for every de-noised signal which was then subtracted from the original SNR. The SNR of the de-noised signal includes the complete signal, taking into account possible over- and undershoot events at the state transitions, which are considered detrimental to subsequent classification.
Sensor Signal and Information Processing Center http://sensip.asu.edu
SenSIP Algorithms and Devices REU
ABSTRACT
REFERENCES
Ion channels are involved with everything from pain to disease in any organism.
Signal processing can be applied to de-noise ion-channel generated signals.
We demonstrate the use of Wavelet analysis and re-synthesis for de-noising, selecting the optimal wavelet base for the particular nature of ion-channel and nanopore signals.
[1] J. Clarke, H. Bayley et al., Nature Nanotechnology 4, 265–270 (2009).
[2] I. M. Derrington, E. Manrao, J. H. Gundlach et al., PNAS 107 (37), 16060-16065 (2010).
[3] S. Choi, M. Goryll, L.Y.M. Sin,et al. Microfluid Nanofluid (2011) 10: 231. doi:10.1007/s10404-010-0638-8
[4] B. Konnanath, P. Sattigeri, T. Mathew, A. Spanias, S.Prasad, M. Goryll, T. J. Thornton , P. Knee, ICANN09
This material is based upon work supported by the National Science Foundation under Grant No. CNS 1659871 REU Site: Sensors, Signal and Information Processing Devices and Algorithms.
Number of wavelet
orders used
Wavelet type
SNR of original signal (dB)
SNR of denoised
signal (dB)
SNR gained or lost from
baseline (dB)
5 Haar 18.29 19.22 0.93 6 Sym 18.29 18.00 -0.29 5 Coif 18.29 21.02 2.73 5 Bior 18.29 19.22 0.93 5 Rbio 18.29 19.22 0.93 4 Dmey 18.29 23.72 5.43 4 Fk 18.29 21.88 3.59
Original Signal
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
Time (ms)
-0.4
1.4
1.2
1
0.8
0.4
0
0.6
0.2
-0.2
Am
plit
ud
e (
a.u
.)
Denoised DMEY
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
Time (ms)
1
0.4
0
0.6
0.2
0.8
Am
plit
ud
e (
a.u
.)
Denoised HAAR
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
Time (ms)
0
0.8
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0.6
0.4
0.5
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0.1
Am
plit
ud
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a.u
.)
0.2
0.9
Am
plit
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.)
Denoised SYM
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
Time (ms)
-0.2
1.2
1
0.8
0.4
0
0.6
0.2
Am
plit
ud
e (
a.u
.)
Denoised FK
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
Time (ms)
1
0.4
0
0.6
0.2
0.8
Am
plit
ud
e (
a.u
.)
Representative signals from biological nanopore sensors used for DNA sequencing: (a) using an a-HL pore [1] and (b) using an MspA pore [2].
Simulated ion channel signal with added white noise using the QuB software package. The red trace shows the idealized signal using a Hidden Markov Model for state classification. (https://qub.mandelics.com/online)
