Deep learning of nanopore sensing signals using a bi-path network

• URL: http://arxiv.org/abs/2105.03660v1
• Date: Sat, 8 May 2021 10:11:22 GMT
• Title: Deep learning of nanopore sensing signals using a bi-path network
• Authors: Dario Dematties, Chenyu Wen, Mauricio David P\'erez, Dian Zhou, Shi-Li Zhang
• Abstract summary: We use deep learning for feature extraction based on a bi-path network (B-Net) The B-Net is capable of processing data with a signal-to-noise ratio equal to one. The developed B-Net is generic for pulse-like signals beyond pulsed nanopore currents.
• Score: 2.365787390596455
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Temporary changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net performance is evaluated on generated datasets and further applied to experimental data of DNA and protein translocation. The B-Net results show remarkably small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to one, an impossibility for threshold-based algorithms. The developed B-Net is generic for pulse-like signals beyond pulsed nanopore currents.

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