Publication detail

Using deep learning for gene detection and classification in raw nanopore signals

NYKRÝNOVÁ, M. JAKUBÍČEK, R. BARTOŇ, V. BEZDÍČEK, M. LENGEROVÁ, M. ŠKUTKOVÁ, H.

Original Title

Using deep learning for gene detection and classification in raw nanopore signals

Type

journal article in Web of Science

Language

English

Original Abstract

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals—squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.

Keywords

nanopore sequencing; squiggles; neural network; MLST; bacterial typing

Authors

NYKRÝNOVÁ, M.; JAKUBÍČEK, R.; BARTOŇ, V.; BEZDÍČEK, M.; LENGEROVÁ, M.; ŠKUTKOVÁ, H.

Released

15. 9. 2022

Publisher

Frontiers Media SA

ISBN

1664-302X

Periodical

Frontiers in Microbiology

Year of study

13

Number

1

State

Swiss Confederation

Pages from

1

Pages to

11

Pages count

11

URL

https://www.frontiersin.org/articles/10.3389/fmicb.2022.942179/full

Full text in the Digital Library

http://hdl.handle.net/11012/208454

BibTex

@article{BUT177652,
  author="Markéta {Nykrýnová} and Roman {Jakubíček} and Vojtěch {Bartoň} and Matěj {Bezdíček} and Martina {Lengerová} and Helena {Vítková}",
  title="Using deep learning for gene detection and classification in raw nanopore signals",
  journal="Frontiers in Microbiology",
  year="2022",
  volume="13",
  number="1",
  pages="1--11",
  doi="10.3389/fmicb.2022.942179",
  issn="1664-302X",
  url="https://www.frontiersin.org/articles/10.3389/fmicb.2022.942179/full"
}