Bista Iliana, Carvalho Gary R, Tang Min, Walsh Kerry, Zhou Xin, Hajibabaei Mehrdad, Shokralla Shadi, Seymour Mathew, Bradley David, Liu Shanlin, Christmas Martin, Creer Simon
School of Biological Sciences, Molecular Ecology and Fisheries Genetics Laboratory, Bangor University, Bangor, UK.
Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.
Mol Ecol Resour. 2018 Apr 18. doi: 10.1111/1755-0998.12888.
New applications of DNA and RNA sequencing are expanding the field of biodiversity discovery and ecological monitoring, yet questions remain regarding precision and efficiency. Due to primer bias, the ability of metabarcoding to accurately depict biomass of different taxa from bulk communities remains unclear, while PCR-free whole mitochondrial genome (mitogenome) sequencing may provide a more reliable alternative. Here, we used a set of documented mock communities comprising 13 species of freshwater macroinvertebrates of estimated individual biomass, to compare the detection efficiency of COI metabarcoding (three different amplicons) and shotgun mitogenome sequencing. Additionally, we used individual COI barcoding and de novo mitochondrial genome sequencing, to provide reference sequences for OTU assignment and metagenome mapping (mitogenome skimming), respectively. We found that, even though both methods occasionally failed to recover very low abundance species, metabarcoding was less consistent, by failing to recover some species with higher abundances, probably due to primer bias. Shotgun sequencing results provided highly significant correlations between read number and biomass in all but one species. Conversely, the read-biomass relationships obtained from metabarcoding varied across amplicons. Specifically, we found significant relationships for eight of 13 (amplicons B1FR-450 bp, FF130R-130 bp) or four of 13 (amplicon FFFR, 658 bp) species. Combining the results of all three COI amplicons (multiamplicon approach) improved the read-biomass correlations for some of the species. Overall, mitogenomic sequencing yielded more informative predictions of biomass content from bulk macroinvertebrate communities than metabarcoding. However, for large-scale ecological studies, metabarcoding currently remains the most commonly used approach for diversity assessment.
DNA和RNA测序的新应用正在拓展生物多样性发现和生态监测领域,但在精度和效率方面仍存在问题。由于引物偏差,宏条形码技术从混合群落中准确描绘不同分类单元生物量的能力尚不清楚,而免PCR的全线粒体基因组(线粒体基因组)测序可能提供更可靠的替代方法。在这里,我们使用了一组记录在案的模拟群落,其中包含13种估计个体生物量的淡水大型无脊椎动物,以比较细胞色素氧化酶亚基I(COI)宏条形码技术(三种不同扩增子)和鸟枪法线粒体基因组测序的检测效率。此外,我们使用个体COI条形码和线粒体基因组从头测序,分别为操作分类单元(OTU)分配和宏基因组图谱绘制(线粒体基因组筛选)提供参考序列。我们发现,尽管两种方法偶尔都无法检测到极低丰度的物种,但宏条形码技术的一致性较差,未能检测到一些丰度较高的物种,这可能是由于引物偏差所致。除了一个物种外,鸟枪法测序结果在所有物种中都显示出读数与生物量之间高度显著的相关性。相反,从宏条形码技术获得的读数-生物量关系在不同扩增子之间有所不同。具体而言,我们发现13个物种中的8个(扩增子B1FR-450 bp、FF130R-130 bp)或13个物种中的4个(扩增子FFFR,658 bp)存在显著关系。结合所有三种COI扩增子的结果(多扩增子方法)提高了一些物种的读数-生物量相关性。总体而言,线粒体基因组测序比宏条形码技术能从大型无脊椎动物群落中获得更丰富的生物量含量信息预测。然而,对于大规模生态研究,宏条形码技术目前仍然是多样性评估中最常用的方法。