SquiggleNet: real-time, direct classification of nanopore signals.
Genome Biol
; 22(1): 298, 2021 10 27.
Article
em En
| MEDLINE
| ID: mdl-34706748
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.
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Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
Sequenciamento por Nanoporos
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article