Cargando…

SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data

BACKGROUND: Structural variations (SVs) refer to variations in an organism’s chromosome structure that exceed a length of 50 base pairs. They play a significant role in genetic diseases and evolutionary mechanisms. While long-read sequencing technology has led to the development of numerous SV calle...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Yan, Shang, Xuequn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207598/
https://www.ncbi.nlm.nih.gov/pubmed/37221476
http://dx.doi.org/10.1186/s12859-023-05324-x
_version_ 1785046492860383232
author Zheng, Yan
Shang, Xuequn
author_facet Zheng, Yan
Shang, Xuequn
author_sort Zheng, Yan
collection PubMed
description BACKGROUND: Structural variations (SVs) refer to variations in an organism’s chromosome structure that exceed a length of 50 base pairs. They play a significant role in genetic diseases and evolutionary mechanisms. While long-read sequencing technology has led to the development of numerous SV caller methods, their performance results have been suboptimal. Researchers have observed that current SV callers often miss true SVs and generate many false SVs, especially in repetitive regions and areas with multi-allelic SVs. These errors are due to the messy alignments of long-read data, which are affected by their high error rate. Therefore, there is a need for a more accurate SV caller method. RESULT: We propose a new method-SVcnn, a more accurate deep learning-based method for detecting SVs by using long-read sequencing data. We run SVcnn and other SV callers in three real datasets and find that SVcnn improves the F1-score by 2–8% compared with the second-best method when the read depth is greater than 5×. More importantly, SVcnn has better performance for detecting multi-allelic SVs. CONCLUSIONS: SVcnn is an accurate deep learning-based method to detect SVs. The program is available at https://github.com/nwpuzhengyan/SVcnn. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05324-x.
format Online
Article
Text
id pubmed-10207598
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-102075982023-05-25 SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data Zheng, Yan Shang, Xuequn BMC Bioinformatics Research BACKGROUND: Structural variations (SVs) refer to variations in an organism’s chromosome structure that exceed a length of 50 base pairs. They play a significant role in genetic diseases and evolutionary mechanisms. While long-read sequencing technology has led to the development of numerous SV caller methods, their performance results have been suboptimal. Researchers have observed that current SV callers often miss true SVs and generate many false SVs, especially in repetitive regions and areas with multi-allelic SVs. These errors are due to the messy alignments of long-read data, which are affected by their high error rate. Therefore, there is a need for a more accurate SV caller method. RESULT: We propose a new method-SVcnn, a more accurate deep learning-based method for detecting SVs by using long-read sequencing data. We run SVcnn and other SV callers in three real datasets and find that SVcnn improves the F1-score by 2–8% compared with the second-best method when the read depth is greater than 5×. More importantly, SVcnn has better performance for detecting multi-allelic SVs. CONCLUSIONS: SVcnn is an accurate deep learning-based method to detect SVs. The program is available at https://github.com/nwpuzhengyan/SVcnn. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05324-x. BioMed Central 2023-05-23 /pmc/articles/PMC10207598/ /pubmed/37221476 http://dx.doi.org/10.1186/s12859-023-05324-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zheng, Yan
Shang, Xuequn
SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
title SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
title_full SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
title_fullStr SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
title_full_unstemmed SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
title_short SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
title_sort svcnn: an accurate deep learning-based method for detecting structural variation based on long-read data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207598/
https://www.ncbi.nlm.nih.gov/pubmed/37221476
http://dx.doi.org/10.1186/s12859-023-05324-x
work_keys_str_mv AT zhengyan svcnnanaccuratedeeplearningbasedmethodfordetectingstructuralvariationbasedonlongreaddata
AT shangxuequn svcnnanaccuratedeeplearningbasedmethodfordetectingstructuralvariationbasedonlongreaddata