Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1784799645511188480 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9520528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT nykrynovamarketa usingdeeplearningforgenedetectionandclassificationinrawnanoporesignals AT jakubicekroman usingdeeplearningforgenedetectionandclassificationinrawnanoporesignals AT bartonvojtech usingdeeplearningforgenedetectionandclassificationinrawnanoporesignals AT bezdicekmatej usingdeeplearningforgenedetectionandclassificationinrawnanoporesignals AT lengerovamartina usingdeeplearningforgenedetectionandclassificationinrawnanoporesignals AT skutkovahelena usingdeeplearningforgenedetectionandclassificationinrawnanoporesignals |