Nichols Ruth V, Vollmers Christopher, Newsom Lee A, Wang Yue, Heintzman Peter D, Leighton McKenna, Green Richard E, Shapiro Beth
Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, California.
Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California.
Mol Ecol Resour. 2018 May 24. doi: 10.1111/1755-0998.12895.
DNA metabarcoding is an increasingly popular method to characterize and quantify biodiversity in environmental samples. Metabarcoding approaches simultaneously amplify a short, variable genomic region, or "barcode," from a broad taxonomic group via the polymerase chain reaction (PCR), using universal primers that anneal to flanking conserved regions. Results of these experiments are reported as occurrence data, which provide a list of taxa amplified from the sample, or relative abundance data, which measure the relative contribution of each taxon to the overall composition of amplified product. The accuracy of both occurrence and relative abundance estimates can be affected by a variety of biological and technical biases. For example, taxa with larger biomass may be better represented in environmental samples than those with smaller biomass. Here, we explore how polymerase choice, a potential source of technical bias, might influence results in metabarcoding experiments. We compared potential biases of six commercially available polymerases using a combination of mixtures of amplifiable synthetic sequences and real sedimentary DNA extracts. We find that polymerase choice can affect both occurrence and relative abundance estimates and that the main source of this bias appears to be polymerase preference for sequences with specific GC contents. We further recommend an experimental approach for metabarcoding based on results of our synthetic experiments.
DNA宏条形码技术是一种在环境样本中表征和量化生物多样性的日益流行的方法。宏条形码方法通过聚合酶链反应（PCR），使用与侧翼保守区域退火的通用引物，同时从广泛的分类群中扩增一个短的、可变的基因组区域，即“条形码”。这些实验的结果报告为出现数据，它提供了从样本中扩增出的分类群列表，或相对丰度数据，它衡量每个分类群对扩增产物总体组成的相对贡献。出现率和相对丰度估计的准确性可能会受到多种生物学和技术偏差的影响。例如，生物量较大的分类群在环境样本中的代表性可能比生物量较小的分类群更好。在这里，我们探讨了聚合酶选择（一种潜在的技术偏差来源）如何可能影响宏条形码实验的结果。我们使用可扩增合成序列混合物和真实沉积DNA提取物的组合，比较了六种市售聚合酶的潜在偏差。我们发现聚合酶选择会影响出现率和相对丰度估计，并且这种偏差的主要来源似乎是聚合酶对具有特定GC含量的序列的偏好。我们还根据合成实验的结果推荐了一种宏条形码实验方法。
