为发现生物合成基因簇而扩展芽孢杆菌的基因组信息。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-21 DOI:10.1038/s41597-024-04118-x
Lijie Song, Lasse Johan Dyrbye Nielsen, Xinming Xu, Omkar Satyavan Mohite, Matin Nuhamunada, Zhihui Xu, Rob Murphy, Kasun Bodawatta, Michael Poulsen, Mohamed Hatha Abdulla, Eva C Sonnenschein, Tilmann Weber, Ákos T Kovács
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引用次数: 0

摘要

这项研究展示了 121 个新的芽孢杆菌基因组,这些基因组来自从全球不同栖息地收集的菌株,使用牛津纳米孔长读数和 MGI 短读数序列组装而成。芽孢杆菌因能产生多种次生代谢物而闻名,这些次生代谢物可用于农业、生物技术和医药领域。这些次生代谢物由生物合成基因簇(smBGCs)编码。smBGCs 具有作为新生物活性化合物来源的潜力,因此具有重大的研究意义。我们的数据集包括 62 个完整基因组、2 个染色体级基因组和 57 个等位基因,基因组大小范围为 3.50 Mb 至 7.15 Mb。系统分类学分析表明,这些基因组跨越了 16 个属,其中 69 个属属于芽孢杆菌。通过硅学基因组挖掘,共鉴定出 1,176 个预测的 BGCs。我们预计,本文提供的开放存取数据将扩大已报道的芽孢杆菌基因组信息,并有助于深入了解芽孢杆菌生产次生代谢物潜力的遗传基础。
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Expanding the genome information on Bacillales for biosynthetic gene cluster discovery.

This study showcases 121 new genomes of spore-forming Bacillales from strains collected globally from a variety of habitats, assembled using Oxford Nanopore long-read and MGI short-read sequences. Bacilli are renowned for their capacity to produce diverse secondary metabolites with use in agriculture, biotechnology, and medicine. These secondary metabolites are encoded within biosynthetic gene clusters (smBGCs). smBGCs have significant research interest due to their potential as sources of new bioactivate compounds. Our dataset includes 62 complete genomes, 2 at chromosome level, and 57 at contig level, covering a genomic size range from 3.50 Mb to 7.15 Mb. Phylotaxonomic analysis revealed that these genomes span 16 genera, with 69 of them belonging to Bacillus. A total of 1,176 predicted BGCs were identified by in silico genome mining. We anticipate that the open-access data presented here will expand the reported genomic information of spore-forming Bacillales and facilitate a deeper understanding of the genetic basis of Bacillales' potential for secondary metabolite production.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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