Chasing Sequencing Perfection: Marching Toward Higher Accuracy and Lower Costs
作者机构：Chinese Acad Sci Inst Zool CAS Key Lab Zool Systemat & Evolut Beijing 100101 Peoples R China Chinese Acad Sci Inst Zool State Key Lab Integrated Management Pest Insects & Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci CAS Ctr Excellence Anim Evolut & Genet Kunming 650223 Peoples R China
出 版 物：《GENOMICS PROTEOMICS & BIOINFORMATICS》 (基因组蛋白质组与生物信息学报（英文版）)
年 卷 期：2024年第22卷第2期
页 面：qzae024页
核心收录：
学科分类：0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 100104[医学-病理学与病理生理学] 10[医学]
基 金：Ministry of Agriculture and Rural Affairs of China National Key R&D Program of China [2019YFA0802600] Chinese Academy of Sciences [ZDBS-LY-SM005, XDPB17] National Natural Science Foundation of China
主 题：Sequencing error High-fidelity sequencing Consensus sequencing Single-molecule sequencing Rare mutation RARE MUTATIONS PARALLEL QUANTIFICATION ERRORS DAMAGE
摘 要：Next-generation sequencing (NGS), represented by Illumina platforms, has been an essential cornerstone of basic and applied research. However, the sequencing error rate of 1 per 1000 base pairs (10-3) represents a serious hurdle for research areas focusing on rare mutations, such as somatic mosaicism or microbe heterogeneity. By examining the high-fidelity sequencing methods developed in the past decade, we summarized three major factors underlying errors and the corresponding 12 strategies mitigating these errors. We then proposed a novel framework to classify 11 preexisting representative methods according to the corresponding combinatory strategies and identified 3 trends that emerged during methodological developments. We further extended this analysis to 8 long-read sequencing methods, emphasizing error reduction strategies. Finally, we suggest 2 promising future directions that could achieve comparable or even higher accuracy with lower costs in both NGS and long-read sequencing.
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