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纳米孔长读测序检测大肠杆菌的从头结构变异。

De Novo Structural Variations of Escherichia coli Detected by Nanopore Long-Read Sequencing.

机构信息

Institute of Evolution and Marine Biodiversity, KLMME, Ocean University of China, Qingdao, Shandong Province, China.

Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA.

出版信息

Genome Biol Evol. 2023 Jun 1;15(6). doi: 10.1093/gbe/evad106.

Abstract

Spontaneous mutations power evolution, whereas large-scale structural variations (SVs) remain poorly studied, primarily because of the lack of long-read sequencing techniques and powerful analytical tools. Here, we explore the SVs of Escherichia coli by running 67 wild-type (WT) and 37 mismatch repair (MMR)-deficient (ΔmutS) mutation accumulation lines, each experiencing more than 4,000 cell divisions, by applying Nanopore long-read sequencing and Illumina PE150 sequencing and verifying the results by Sanger sequencing. In addition to precisely repeating previous mutation rates of base-pair substitutions and insertion and deletion (indel) mutation rates, we do find significant improvement in insertion and deletion detection using long-read sequencing. The long-read sequencing and corresponding software can particularly detect bacterial SVs in both simulated and real data sets with high accuracy. These lead to SV rates of 2.77 × 10-4 (WT) and 5.26 × 10-4 (MMR-deficient) per cell division per genome, which is comparable with previous reports. This study provides the SV rates of E. coli by applying long-read sequencing and SV detection programs, revealing a broader and more accurate picture of spontaneous mutations in bacteria.

摘要

自发突变推动了进化,而大规模结构变异(SVs)仍然研究不足,主要是因为缺乏长读测序技术和强大的分析工具。在这里,我们通过应用纳米孔长读测序和 Illumina PE150 测序,对 67 条野生型(WT)和 37 条错配修复(MMR)缺陷(ΔmutS)突变积累系进行了研究,每个系经历了超过 4000 次细胞分裂,并通过桑格测序进行了验证,从而探索了大肠杆菌的 SVs。除了精确地重复以前的碱基对替换和插入和缺失(indel)突变率的突变率外,我们确实发现长读测序在插入和缺失检测方面有显著提高。长读测序和相应的软件可以特别高的准确性检测模拟和真实数据集的细菌 SVs。这些导致每个细胞分裂每个基因组的 SV 率分别为 2.77×10-4(WT)和 5.26×10-4(MMR 缺陷),与之前的报告相当。这项研究通过应用长读测序和 SV 检测程序提供了大肠杆菌的 SV 率,揭示了细菌自发突变更广泛和更准确的图景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73a6/10292909/7fd2673c6645/evad106f1.jpg

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