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Sorting things out: Assessing effects of unequal specimen biomass on DNA metabarcoding

机译:整理:评估不相等的标本生物量对DNA元条形码的影响

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Abstract Environmental bulk samples often contain many different taxa that vary several orders of magnitude in biomass. This can be problematic in DNA metabarcoding and metagenomic high-throughput sequencing approaches, as large specimens contribute disproportionately high amounts of DNA template. Thus, a few specimens of high biomass will dominate the dataset, potentially leading to smaller specimens remaining undetected. Sorting of samples by specimen size (as a proxy for biomass) and balancing the amounts of tissue used per size fraction should improve detection rates, but this approach has not been systematically tested. Here, we explored the effects of size sorting on taxa detection using two freshwater macroinvertebrate bulk samples, collected from a low-mountain stream in Germany. Specimens were morphologically identified and sorted into three size classes (body size < 2.5 ???? 5, 5 ???? 10, and up to 10 ???? 20 mm). Tissue powder from each size category was extracted individually and pooled based on tissue weight to simulate samples that were not sorted by biomass (?¢????Unsorted?¢????). Additionally, size fractions were pooled so that each specimen contributed approximately equal amounts of biomass (?¢????Sorted?¢????). Mock samples were amplified using four different DNA metabarcoding primer sets targeting the Cytochrome c oxidase I (COI) gene. Sorting taxa by size and pooling them proportionately according to their abundance lead to a more equal amplification of taxa compared to the processing of complete samples without sorting. The sorted samples recovered 30% more taxa than the unsorted samples at the same sequencing depth. Our results imply that sequencing depth can be decreased approximately fivefold when sorting the samples into three size classes and pooling by specimen abundance. Even coarse size sorting can substantially improve taxa detection using DNA metabarcoding. While high-throughput sequencing will become more accessible and cheaper within the next years, sorting bulk samples by specimen biomass or size is a simple yet efficient method to reduce current sequencing costs.
机译:摘要环境散装样品通常包含许多不同的分类单元,其生物量变化几个数量级。这在DNA元条形码和宏基因组学高通量测序方法中可能会出现问题,因为大型标本贡献了不成比例的大量DNA模板。因此,少量的高生物量标本将主导数据集,可能导致较小的标本仍未被发现。按样本大小对样品进行分类(作为生物量的替代物)并平衡每个大小部分所使用的组织数量应提高检出率,但是这种方法尚未得到系统地测试。在这里,我们使用两个淡水大型无脊椎动物大宗样品(从德国的一个低山川中采集)来探索大小分类对分类群检测的影响。样品在形态上被鉴定并分为三个尺寸等级(体尺寸<2.5≤5、5≤10,最大10≤20 mm)。分别提取每种尺寸类别的组织粉并根据组织重量合并以模拟未按生物质分类的样品(“未分类”)。另外,将大小分数合并,以使每个样品贡献大约等量的生物质(排序)。使用靶向细胞色素c氧化酶I(COI)基因的四种不同的DNA元条形码引物组扩增模拟样品。与未分类的完整样本处理相比,按大小对分类单元进行分类,并根据其丰度按比例将它们合并起来,可以更均匀地放大分类单元。在相同的测序深度下,分类的样本比未分类的样本能多回收30%的分类单元。我们的结果表明,将样本分为三个大小级别并按样本丰度进行合并时,测序深度可以降低大约五倍。即使是粗粒度分类,也可以使用DNA元条形码显着改善分类单位检测。虽然高通量测序将在未来几年内变得更加容易和便宜,但按标本生物量或大小分选大样本是降低当前测序成本的一种简单而有效的方法。

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