Learning shapelets for improving the single-molecule nanopore sensing

The nanopore technique employs a nanoscale cavity to electrochemically confine individual molecules, achieving ultrasen-sitive single-molecule analysis based on evaluating the amplitude and duration of the ionic current. However, each na-nopore sensing interface has its own intrinsic sensing ability, which does not always efficiently generate distinctive block-ade currents for multiple analytes. Therefore, analytes that differ at only a single site often exhibit similar blockade cur-rents or durations in nanopore experiments, which often produces serious overlap in the resulting statistical graphs. To improve the sensing ability of nanopores, herein, we propose a novel shapelet-based machine learning approach to dis-criminate mixed analytes that exhibit nearly identical blockade current amplitudes and durations. DNA oligomers with a single-nucleotide difference, 5’-AAAA-3’ and 5’-GAAA-3’, are employed as model analytes that are difficult to identify in aerolysin nanopores at 100 mV. First, a set of the mo...