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Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias

机译:昆虫的环境元条形码:计算机PCR显示潜在的生物分类偏见

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Studies of insect assemblages are suited to the simultaneous DNA-based identification of multiple taxa known as metabarcoding. To obtain accurate estimates of diversity, metabarcoding markers ideally possess appropriate taxonomic coverage to avoid PCR-amplification bias, as well as sufficient sequence divergence to resolve species. We used in silico PCR to compare the taxonomic coverage and resolution of newly designed insect metabarcodes (targeting 16S) with that of existing markers [16S and cytochrome oxidase c subunit I (COI)] and then compared their efficiency in vitro. Existing metabarcoding primers amplified in silico <75% of insect species with complete mitochondrial genomes available, whereas new primers targeting 16S provided >90% coverage. Furthermore, metabarcodes targeting COI appeared to introduce taxonomic PCR-amplification bias, typically amplifying a greater percentage of Lepidoptera and Diptera species, while failing to amplify certain orders in silico. To test whether bias predicted in silico was observed in vitro, we created an artificial DNA blend containing equal amounts of DNA from 14 species, representing 11 insect orders and one arachnid. We PCR-amplified the blend using five primer sets, targeting either COI or 16S, with high-throughput amplicon sequencing yielding more than 6 million reads. In vitro results typically corresponded to in silico PCR predictions, with newly designed 16S primers detecting 11 insect taxa present, thus providing equivalent or better taxonomic coverage than COI metabarcodes. Our results demonstrate that in silico PCR is a useful tool for predicting taxonomic bias in mixed template PCR and that researchers should be wary of potential bias when selecting metabarcoding markers.
机译:昆虫组合的研究适合于同时基于DNA的多种类群的识别,称为metabarcoding。为了获得准确的多样性估计值,理想的是,元条形码标记具有适当的分类学覆盖范围,以避免PCR扩增偏倚以及足够的序列差异来分辨物种。我们在计算机PCR中使用了PCR技术,比较了新设计的昆虫meta条形码(针对16S)与现有标记[16S和细胞色素氧化酶C亚基I(COI)]的分类学覆盖率和分辨率,然后比较了它们的体外效率。现有的metabarcoding引物可在计算机上<75%的昆虫物种中扩增并具有完整的线粒体基因组,而靶向16S的新引物可提供> 90%的覆盖率。此外,靶向COI的元条形码似乎引入了分类学PCR扩增偏倚,通常会放大较大比例的鳞翅目和双翅目物种,而无法放大计算机的某些顺序。为了测试是否在体外观察到了计算机模拟中的偏见,我们创建了一种人工DNA混合物,其中包含来自14种物种的等量DNA,分别代表11个昆虫纲和一个蜘蛛纲。我们使用针对COI或16S的五对引物对PCR混合物进行了PCR扩增,高通量扩增子测序产生了超过600万条读数。体外结果通常与计算机PCR预测相对应,新设计的16S引物可检测到11种昆虫类群,因此与COI元条形码相比,具有同等或更好的分类覆盖率。我们的结果表明,计算机PCR是预测混合模板PCR中分类偏倚的有用工具,研究人员在选择元条形码标记时应警惕潜在的偏倚。

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