SquiggleNet: real-time, direct classification of nanopore signals

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 achi...

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Bibliographic Details
Main Authors: Yuwei Bao, Jack Wadden, John R. Erb-Downward, Piyush Ranjan, Weichen Zhou, Torrin L. McDonald, Ryan E. Mills, Alan P. Boyle, Robert P. Dickson, David Blaauw, Joshua D. Welch
Format: Article
Language:English
Published: BMC 2021-10-01
Series:Genome Biology
Subjects:
Deep learning
Read-until
Oxford Nanopore
Raw signal
Real-time
Online Access:https://doi.org/10.1186/s13059-021-02511-y

Internet

https://doi.org/10.1186/s13059-021-02511-y

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