The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes.

IF 3.7 Q2 GENETICS & HEREDITY
Gong Zhang, Yongjian Zhang, Jingjie Jin
{"title":"The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes.","authors":"Gong Zhang,&nbsp;Yongjian Zhang,&nbsp;Jingjie Jin","doi":"10.1007/s43657-020-00008-5","DOIUrl":null,"url":null,"abstract":"<p><p>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<sup>-9</sup> 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/.</p>","PeriodicalId":74435,"journal":{"name":"Phenomics (Cham, Switzerland)","volume":"1 1","pages":"22-30"},"PeriodicalIF":3.7000,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s43657-020-00008-5","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Phenomics (Cham, Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s43657-020-00008-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 12

Abstract

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/.

Abstract Image

Abstract Image

Abstract Image

超快速和精确的绘图算法FANSe3:在30分钟内绘制人类全基因组测序数据集。
在大多数NGS应用中,将下一代测序(NGS)产生的数十亿个reads与参考序列进行比对(称为“mapping”)是耗时且计算密集型的过程。需要一种快速、准确、鲁棒的映射算法。因此,我们开发了FANSe3图谱算法,该算法可以在单服务器节点上30分钟内绘制一个30 ×人类全基因组测序(WGS)数据集,30秒内绘制一个50 ×人类全外显子组测序(WES)数据集,几秒内绘制一个典型的mRNA-seq数据集,而无需任何硬件加速功能。与其前身FANSe2一样,FANSe3的错误率在大多数情况下可以保持在10-9以下,这比基于Burrows-Wheeler变换的算法更健壮。在临床相关的WGS数据中,误差容限几乎不影响驱动体细胞突变的识别,并且无论参数设置和使用的测序仪如何,都提供了健壮的基因表达谱。该算法专为基础设施后的高性能云计算而设计,将打破NGS数据分析速度和精度的瓶颈,推动NGS在各个领域的应用。FANSe3算法可从网站http://www.chi-biotech.com/fanse3/下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信