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

TL;DR

This paper introduces a bi-path deep learning network (B-Net) that automatically extracts features from nanopore sensing signals, outperforming traditional threshold-based methods especially in noisy conditions.

Contribution

The paper presents a novel deep learning approach using B-Net for objective pulse recognition and feature extraction in nanopore signals, eliminating the need for empirical thresholds.

Findings

B-Net achieves small relative errors in pulse recognition.

B-Net effectively processes signals with low signal-to-noise ratio.

The method is applicable to various pulse-like signals beyond nanopores.

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.