Detail publikačního výsledku

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

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

Originální název

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

Anglický název

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

Druh

Článek WoS

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

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

Rok RIV

2023

Vydáno

15.09.2022

Nakladatel

Frontiers Media SA

ISSN

1664-302X

Periodikum

Frontiers in Microbiology

Svazek

13

Číslo

1

Stát

Švýcarská konfederace

Strany od

1

Strany do

11

Strany počet

11

URL

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

Plný text v Digitální knihovně

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

BibTex

@article{BUT177652,
  author="Markéta {Jakubíčková} 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"
}

Dokumenty

fmicb-13-942179