SquiggleNet: real-time, direct classification of nanopore signals
Yuwei Bao, Jack Wadden, John R. Erb‐Downward, Piyush Ranjan, Weichen Zhou, T. L. McDonald, Ryan E. Mills, Alan P. Boyle, Robert P. Dickson, David Blaauw, Joshua D. Welch
Abstract
We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.