Fast custom wavelet analysis technique for single molecule detection and identification

Vahid Ganjalizadeh, Gopikrishnan G. Meena, Thomas A. Wall, Matthew A. Stott, Aaron R. Hawkins and Holger Schmidt ()
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Vahid Ganjalizadeh: University of California, Santa Cruz
Gopikrishnan G. Meena: University of California, Santa Cruz
Thomas A. Wall: Brigham Young University
Matthew A. Stott: Brigham Young University
Aaron R. Hawkins: Brigham Young University
Holger Schmidt: University of California, Santa Cruz

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.

Date: 2022
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DOI: 10.1038/s41467-022-28703-z

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