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Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection

Nanopore sequencing, also known as single-molecule real-time sequencing, is a third/fourth generation sequencing technology that enables deciphering single DNA/RNA molecules without the polymerase chain reaction. Although nanopore sequencing has made significant progress in scientific research and c...

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Autores principales: Lang, Jidong, Sun, Jiguo, Yang, Zhi, He, Lei, He, Yu, Chen, Yanmei, Huang, Lei, Li, Ping, Li, Jialin, Qin, Liu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022462/
https://www.ncbi.nlm.nih.gov/pubmed/35464239
http://dx.doi.org/10.1093/nargab/lqac033
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author Lang, Jidong
Sun, Jiguo
Yang, Zhi
He, Lei
He, Yu
Chen, Yanmei
Huang, Lei
Li, Ping
Li, Jialin
Qin, Liu
author_facet Lang, Jidong
Sun, Jiguo
Yang, Zhi
He, Lei
He, Yu
Chen, Yanmei
Huang, Lei
Li, Ping
Li, Jialin
Qin, Liu
author_sort Lang, Jidong
collection PubMed
description Nanopore sequencing, also known as single-molecule real-time sequencing, is a third/fourth generation sequencing technology that enables deciphering single DNA/RNA molecules without the polymerase chain reaction. Although nanopore sequencing has made significant progress in scientific research and clinical practice, its application has been limited compared with next-generation sequencing (NGS) due to specific design principle and data characteristics, especially in hotspot mutation detection. Therefore, we developed Nano2NGS-Muta as a data analysis framework for hotspot mutation detection based on long reads from nanopore sequencing. Nano2NGS-Muta is characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. Long reads can be converted into short reads and then processed through existing NGS analysis pipelines in combination with statistical methods for hotspot mutation detection. Nano2NGS-Muta not only effectively avoids false positive/negative results caused by non-random errors and unexpected insertions-deletions (indels) of nanopore sequencing data, improves the detection accuracy of hotspot mutations compared to conventional nanopore sequencing data analysis algorithms but also breaks the barriers of data analysis methods between short-read sequencing and long-read sequencing. We hope Nano2NGS-Muta can serves as a reference method for nanopore sequencing data and promotes higher application scope of nanopore sequencing technology in scientific research and clinical practice.
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spelling pubmed-90224622022-04-21 Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection Lang, Jidong Sun, Jiguo Yang, Zhi He, Lei He, Yu Chen, Yanmei Huang, Lei Li, Ping Li, Jialin Qin, Liu NAR Genom Bioinform Methods Article Nanopore sequencing, also known as single-molecule real-time sequencing, is a third/fourth generation sequencing technology that enables deciphering single DNA/RNA molecules without the polymerase chain reaction. Although nanopore sequencing has made significant progress in scientific research and clinical practice, its application has been limited compared with next-generation sequencing (NGS) due to specific design principle and data characteristics, especially in hotspot mutation detection. Therefore, we developed Nano2NGS-Muta as a data analysis framework for hotspot mutation detection based on long reads from nanopore sequencing. Nano2NGS-Muta is characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. Long reads can be converted into short reads and then processed through existing NGS analysis pipelines in combination with statistical methods for hotspot mutation detection. Nano2NGS-Muta not only effectively avoids false positive/negative results caused by non-random errors and unexpected insertions-deletions (indels) of nanopore sequencing data, improves the detection accuracy of hotspot mutations compared to conventional nanopore sequencing data analysis algorithms but also breaks the barriers of data analysis methods between short-read sequencing and long-read sequencing. We hope Nano2NGS-Muta can serves as a reference method for nanopore sequencing data and promotes higher application scope of nanopore sequencing technology in scientific research and clinical practice. Oxford University Press 2022-04-21 /pmc/articles/PMC9022462/ /pubmed/35464239 http://dx.doi.org/10.1093/nargab/lqac033 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Article
Lang, Jidong
Sun, Jiguo
Yang, Zhi
He, Lei
He, Yu
Chen, Yanmei
Huang, Lei
Li, Ping
Li, Jialin
Qin, Liu
Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection
title Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection
title_full Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection
title_fullStr Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection
title_full_unstemmed Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection
title_short Nano2NGS-Muta: a framework for converting nanopore sequencing data to NGS-liked sequencing data for hotspot mutation detection
title_sort nano2ngs-muta: a framework for converting nanopore sequencing data to ngs-liked sequencing data for hotspot mutation detection
topic Methods Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022462/
https://www.ncbi.nlm.nih.gov/pubmed/35464239
http://dx.doi.org/10.1093/nargab/lqac033
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