SquiggleNet: real-time, direct classification of nanopore signals

Genome Biol. 2021 Oct 27;22(1):298. doi: 10.1186/s13059-021-02511-y.


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.

Keywords: Deep learning; Oxford Nanopore; Raw signal; Read-until; Real-time.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • DNA, Bacterial / analysis
  • Deep Learning*
  • Humans
  • Long Interspersed Nucleotide Elements
  • Metagenome
  • Nanopore Sequencing / methods*
  • Respiratory System / microbiology


  • DNA, Bacterial