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
{"title":"为发现生物合成基因簇而扩展芽孢杆菌的基因组信息。","authors":"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","doi":"10.1038/s41597-024-04118-x","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1267"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582795/pdf/","citationCount":"0","resultStr":"{\"title\":\"Expanding the genome information on Bacillales for biosynthetic gene cluster discovery.\",\"authors\":\"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\",\"doi\":\"10.1038/s41597-024-04118-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"11 1\",\"pages\":\"1267\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582795/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-024-04118-x\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04118-x","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
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.
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.