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GigabyteGenomicsPeer-reviewed journal article

Efficiently Constructing Complete Genomes with CycloneSEQ to Fill Gaps in Bacterial Draft Assemblies

Hewei LiangiD, Yuanqiang ZouiD, Mengmeng WangiD, Tongyuan HuiD, Haoyu WangiD, Wenxin HeiD, Yanmei JuiD, Ruijin GuoiD, Junyi CheniD, Fei GuoiD, Tao ZengiD, Yuliang DongiD, Yuning ZhangiD, Bo WangiD, Chuanyu LiuiD, Xin JiniD, Wenwei ZhangiD, Xun XuiD, Liang XiaoiD

PDFDOIPreprint

Abstract

BackgroundCurrent microbial sequencing relies on short-read platforms like Illumina and DNBSEQ, favored for their low cost and high accuracy. However, these methods often produce fragmented draft genomes, hindering comprehensive bacterial function analysis. CycloneSEQ, a novel long-read sequencing platform developed by BGI-Research, its sequencing performance and assembly improvements has been evaluated. FindingsUsing CycloneSEQ long-read sequencing, the type strain produced long reads with an average length of 11.6 kbp and an average quality score of 14.4. After hybrid assembly with short reads data, the assembled genome exhibited an error rate of only 0.04 mismatches and 0.08 indels per 100 kbp compared to the reference genome. This method was validated across 9 diverse species, successfully assembling complete circular genomes. Hybrid assembly significantly enhances genome completeness by using long reads to fill gaps and accurately assemble multi-copy rRNA genes, which unable be achieved by short reads solely. Through data subsampling, we found that over 500 Mbp of short-read data combined with 100 Mbp of long-read data can result in a high-quality circular assembly. Additionally, using CycloneSEQ long reads effectively improves the assembly of circular complete genomes from mixed microbial communities. ConclusionsCycloneSEQs read length is sufficient for circular bacterial genomes, but its base quality needs improvement. Integrating DNBSEQ short reads improved accuracy, resulting in complete and accurate assemblies. This efficient approach can be widely applied in microbial sequencing.

Citations
22
FWCI
6.64
Field percentile
top 2%

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