Graduation Semester and Year
Summer 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Biomedical Engineering
Department
Bioengineering
First Advisor
George Alexandrakis
Second Advisor
Khosrow Behbehani
Third Advisor
Salman Sohrabi
Abstract
Nanopore biosensors have played an essential role in the scientific investigation of DNA, RNA, proteins, and other bio-analytes. Traditional nanopore biosensors detect variations in electrical current conductance caused by the analyte blocking the current passing transiently through a nano-sized aperture, i.e., the nanopore. This electrical data provides information about the amount of current blockage when the analyte is inside the nanopore and the time it takes for analytes to travel through the nanopore (translocation time). The translocation time of analytes in standard nanopore measurements is typically very short, usually tens of microseconds, limiting the accuracy of measuring the analytes’ characteristics. The Self-induced Back Action Actuated Nanopore Electrophoresis (SANE) biosensor slows analyte translocation time by optically trapping analytes through the Self-Induced Back Action (SIBA) mechanism. The SANE sensor combines optical trapping and electrophoresis to trap and detect analytes. This allows us to slow down analytes and observe them for several seconds, which is a much more extended period compared to traditional electrical nanopore techniques. The SANE sensor data acquisition hardware contained four channels: two for electrical and two for optical data. These data provide us with essential features in signal processing, such as optical step change, optical trap time, translocation time, and translocation current.
Previous developed MATLAB-based GUIs such as “EventPro: Nanopore Data Analysis App” and “AutoStepfinder” have already been developed by other researchers to process and analyze nanopore electrical data. This MSc thesis focuses on developing computer code functions for signal signature identification, processing, and quantification, specifically for the optical data obtained from the SANE sensor, as no satisfactory methods could be borrowed and used successfully from the existing scientific literature without modification. Specifically, this work focuses on the development and performance comparison between three different methods: (1) an extension of the previously published “AutoStepfinder” method developed by Loeff et al., (2) Bandpass filtering using one the Chebyshev, Elliptical, Butterworth, or Sinc filters, followed by a peak-finding algorithm, and (3) a convolutional filter method. The data processed using 'AutoStepfinder' suggests that although it could be an effective tool for processing multi-step data, it tends to detects small optical signal step changes, which are noise, as real steps. Therefore, the results from the 'AutoStepfinder' generate too many false positive data. In contrast, the results obtained from bandpass filtering, using a Sinc function, followed by a peak detection algorithm show high similarity to the manually identified optical signal step-changes, used as the gold standard for comparisons in this work. However, limitations exist, such as the need to apply thresholds for peak detection, which prevents the identification of peaks near the baseline noise level. The convolutional method is still being developed and requires further optimization for optical signal processing.
Keywords
Nanopore Biosensors, SANE Sensor, Signal Processing, Data Analysis, Optical Signal, Signal Signature Identification
Disciplines
Bioimaging and Biomedical Optics | Other Biomedical Engineering and Bioengineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Huang, Yu-Shiuan, "ALGORITHM DEVELOPMENT FOR ANALYSIS OF OPTICAL TIME-SERIES DATA GENERATED BY BIOMODAL OPTICAL-ELECTRICAL NANOPORE SENSOR" (2024). Bioengineering Theses. 188.
https://mavmatrix.uta.edu/bioengineering_theses/188
Included in
Bioimaging and Biomedical Optics Commons, Other Biomedical Engineering and Bioengineering Commons