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The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes
Aligning billions of reads generated by the next-generation sequencing (NGS) to reference sequences, termed “mapping”, is the time-consuming and computationally-intensive process in most NGS applications. A Fast, accurate and robust mapping algorithm is highly needed. Therefore, we developed the FAN...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584123/ https://www.ncbi.nlm.nih.gov/pubmed/36939746 http://dx.doi.org/10.1007/s43657-020-00008-5 |
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author | Zhang, Gong Zhang, Yongjian Jin, Jingjie |
author_facet | Zhang, Gong Zhang, Yongjian Jin, Jingjie |
author_sort | Zhang, Gong |
collection | PubMed |
description | Aligning billions of reads generated by the next-generation sequencing (NGS) to reference sequences, termed “mapping”, is the time-consuming and computationally-intensive process in most NGS applications. A Fast, accurate and robust mapping algorithm is highly needed. Therefore, we developed the FANSe3 mapping algorithm, which can map a 30 × human whole-genome sequencing (WGS) dataset within 30 min, a 50 × human whole exome sequencing (WES) dataset within 30 s, and a typical mRNA-seq dataset within seconds in a single-server node without the need for any hardware acceleration feature. Like its predecessor FANSe2, the error rate of FANSe3 can be kept as low as 10(–9) in most cases, this is more robust than the Burrows–Wheeler transform-based algorithms. Error allowance hardly affected the identification of a driver somatic mutation in clinically relevant WGS data and provided robust gene expression profiles regardless of the parameter settings and sequencer used. The novel algorithm, designed for high-performance cloud-computing after infrastructures, will break the bottleneck of speed and accuracy in NGS data analysis and promote NGS applications in various fields. The FANSe3 algorithm can be downloaded from the website: http://www.chi-biotech.com/fanse3/. |
format | Online Article Text |
id | pubmed-9584123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-95841232022-10-26 The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes Zhang, Gong Zhang, Yongjian Jin, Jingjie Phenomics Article Aligning billions of reads generated by the next-generation sequencing (NGS) to reference sequences, termed “mapping”, is the time-consuming and computationally-intensive process in most NGS applications. A Fast, accurate and robust mapping algorithm is highly needed. Therefore, we developed the FANSe3 mapping algorithm, which can map a 30 × human whole-genome sequencing (WGS) dataset within 30 min, a 50 × human whole exome sequencing (WES) dataset within 30 s, and a typical mRNA-seq dataset within seconds in a single-server node without the need for any hardware acceleration feature. Like its predecessor FANSe2, the error rate of FANSe3 can be kept as low as 10(–9) in most cases, this is more robust than the Burrows–Wheeler transform-based algorithms. Error allowance hardly affected the identification of a driver somatic mutation in clinically relevant WGS data and provided robust gene expression profiles regardless of the parameter settings and sequencer used. The novel algorithm, designed for high-performance cloud-computing after infrastructures, will break the bottleneck of speed and accuracy in NGS data analysis and promote NGS applications in various fields. The FANSe3 algorithm can be downloaded from the website: http://www.chi-biotech.com/fanse3/. Springer Singapore 2021-02-22 /pmc/articles/PMC9584123/ /pubmed/36939746 http://dx.doi.org/10.1007/s43657-020-00008-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . |
spellingShingle | Article Zhang, Gong Zhang, Yongjian Jin, Jingjie The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes |
title | The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes |
title_full | The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes |
title_fullStr | The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes |
title_full_unstemmed | The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes |
title_short | The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes |
title_sort | ultrafast and accurate mapping algorithm fanse3: mapping a human whole-genome sequencing dataset within 30 minutes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584123/ https://www.ncbi.nlm.nih.gov/pubmed/36939746 http://dx.doi.org/10.1007/s43657-020-00008-5 |
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