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

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 assem...

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Autores principales: Nykrynova, Marketa, Jakubicek, Roman, Barton, Vojtech, Bezdicek, Matej, Lengerova, Martina, Skutkova, Helena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520528/
https://www.ncbi.nlm.nih.gov/pubmed/36187947
http://dx.doi.org/10.3389/fmicb.2022.942179
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author Nykrynova, Marketa
Jakubicek, Roman
Barton, Vojtech
Bezdicek, Matej
Lengerova, Martina
Skutkova, Helena
author_facet Nykrynova, Marketa
Jakubicek, Roman
Barton, Vojtech
Bezdicek, Matej
Lengerova, Martina
Skutkova, Helena
author_sort Nykrynova, Marketa
collection PubMed
description 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.
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spelling pubmed-95205282022-09-30 Using deep learning for gene detection and classification in raw nanopore signals Nykrynova, Marketa Jakubicek, Roman Barton, Vojtech Bezdicek, Matej Lengerova, Martina Skutkova, Helena Front Microbiol Microbiology 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. Frontiers Media S.A. 2022-09-15 /pmc/articles/PMC9520528/ /pubmed/36187947 http://dx.doi.org/10.3389/fmicb.2022.942179 Text en Copyright © 2022 Nykrynova, Jakubicek, Barton, Bezdicek, Lengerova and Skutkova. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Nykrynova, Marketa
Jakubicek, Roman
Barton, Vojtech
Bezdicek, Matej
Lengerova, Martina
Skutkova, Helena
Using deep learning for gene detection and classification in raw nanopore signals
title Using deep learning for gene detection and classification in raw nanopore signals
title_full Using deep learning for gene detection and classification in raw nanopore signals
title_fullStr Using deep learning for gene detection and classification in raw nanopore signals
title_full_unstemmed Using deep learning for gene detection and classification in raw nanopore signals
title_short Using deep learning for gene detection and classification in raw nanopore signals
title_sort using deep learning for gene detection and classification in raw nanopore signals
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520528/
https://www.ncbi.nlm.nih.gov/pubmed/36187947
http://dx.doi.org/10.3389/fmicb.2022.942179
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