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SquiggleNet: real-time, direct classification of nanopore signals.

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.

SUBMITTER: Bao Y

PROVIDER: S-EPMC8548853 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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SquiggleNet: real-time, direct classification of nanopore signals.

Bao Yuwei Y Wadden Jack J Erb-Downward John R JR Ranjan Piyush P Zhou Weichen W McDonald Torrin L TL Mills Ryan E RE Boyle Alan P AP Dickson Robert P RP Blaauw David D Welch Joshua D JD

Genome biology 20211027 1

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 dist ...[more]

PMID: 34706748

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