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Comparative genomics of 274 Vibrio cholerae genomes reveals mobile functions structuring three niche dimensions

机译:274个霍乱弧菌基因组的比较基因组学揭示了构成三个生态位的移动功能

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Background Vibrio cholerae is a globally dispersed pathogen that has evolved with humans for centuries, but also includes non-pathogenic environmental strains. Here, we identify the genomic variability underlying this remarkable persistence across the three major niche dimensions space, time, and habitat. Results Taking an innovative approach of genome-wide association applicable to microbial genomes (GWAS-M), we classify 274 complete V. cholerae genomes by niche, including 39 newly sequenced for this study with the Ion Torrent DNA-sequencing platform. Niche metadata were collected for each strain and analyzed together with comprehensive annotations of genetic and genomic attributes, including point mutations (single-nucleotide polymorphisms, SNPs), protein families, functions and prophages. Conclusions Our analysis revealed that genomic variations, in particular mobile functions including phages, prophages, transposable elements, and plasmids underlie the metadata structuring in each of the three niche dimensions. This underscores the role of phages and mobile elements as the most rapidly evolving elements in bacterial genomes, creating local endemicity (space), leading to temporal divergence (time), and allowing the invasion of new habitats. Together, we take a data-driven approach for comparative functional genomics that exploits high-volume genome sequencing and annotation, in conjunction with novel statistical and machine learning analyses to identify connections between genotype and phenotype on a genome-wide scale.
机译:背景霍乱弧菌是一种在全球范围内传播的病原体,已与人类一起进化了几个世纪,但也包括非致病性环境菌株。在这里,我们确定了在三个主要利基维度空间,时间和栖息地上具有这种显着持久性的基因组变异性。结果采用适用于微生物基因组的全基因组关联的创新方法(GWAS-M),我们通过利基分类法对274个完整的霍乱弧菌基因组进行了分类,其中包括39个通过离子激流DNA测序平台进行本研究的新测序。收集每个菌株的利基元数据,并与遗传和基因组属性的全面注释一起进行分析,包括点突变(单核苷酸多态性,SNP),蛋白质家族,功能和原噬菌体。结论我们的分析表明,基因组变异,特别是包括噬菌体,噬菌体,转座因子和质粒在内的移动功能,是这三个利基维度中元数据结构的基础。这强调了噬菌体和移动元件作为细菌基因组中发展最快的元件的作用,产生了地方性流行病(空间),导致了时间差异(时间),并允许新的生境的入侵。我们共同采取了一种数据驱动的比较功能基因组学方法,该方法利用了高容量的基因组测序和注释,并结合了新颖的统​​计和机器学习分析,可以确定基因组范围内基因型和表型之间的联系。

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