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Species detection and delineation in the marine planktonic diatoms Chaetoceros and Bacteriastrum through metabarcoding: making biological sense of haplotype diversity

机译:通过Metabarcoding的海洋浮游硅藻土和诱导细菌的物种检测和描绘:制定单倍型多样性的生物学意义

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摘要

High-throughput sequencing (HTS) metabarcoding is commonly applied to assess phytoplankton diversity. Usually, haplotypes are grouped into operational taxonomic units (OTUs) through clustering, whereby the resulting number of OTUs depends on chosen similarity thresholds. We applied, instead, a phylogenetic approach to infer taxa among 18S rDNA V4-metabarcode haplotypes gathered from 48 time-series samples using the marine planktonic diatoms Chaetoceros and Bacteriastrum as test case. The 73 recovered taxa comprised both solitary haplotypes and polytomies, the latter composed each of a highly abundant, dominant haplotype and one to several minor, peripheral haplotypes. The solitary and dominant haplotypes usually matched reference sequences, enabling species assignation of taxa. We hypothesise that the super-abundance of reads in dominant haplotypes results from the homogenization effect of concerted evolution. Reads of populous peripheral haplotypes and dominant haplotypes show comparable distribution patterns over the sample dates, suggesting that they are part of the same population. Many taxa revealed marked seasonality, with closely related ones generally showing distinct periodicity, whereas others occur year-round. Phylogenies inferred from metabarcode haplotypes enable delineation of biologically meaningful taxa, whereas OTUs resulting from clustering algorithms often deviate markedly from such taxa.
机译:通常适用于评估浮游植物的多样性的高通量测序(HTS)元序列。通常,单倍型通过聚类分组成运作分类单位(OTUS),由此产生的OTU数量取决于所选的相似阈值。相反,我们应用了从48个时间序列样品中收集的18秒rdna v4-metabarcode单倍型中的系统发育方法,从48次序列样本中使用海洋氏菌硅藻色谱和菌作为测试用例。 73个回收的分类群包括孤立性单型和多元素,后者组成了高度丰富,优势的单倍型和一对一的次要外围单倍型。孤独和占优势的单倍型通常匹配参考序列,使物种分配出分类。我们假设显性单倍型中的读取超丰度是由协同演化的均质效果产生的。读取人口般的外围单倍型和显性单倍型显示出样品日期的可比分布模式,表明它们是相同人群的一部分。许多分类群揭示了明显的季节性,密切相关的季节性通常表现出明显的周期,而其他国家则全年出现。从Metabarcode Haplotypes推断的文学发电机构能够划定生物学有意义的分类群,而聚类算法产生的OTUS通常从这些分类群中显着偏离。

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