1. Despite widespread recognition of its great promise to aid decision-making in environmental management, the applied use of metabarcoding requires improvements to reduce the multiple errors that arise during PCR amplification, sequencing and library generation. We present a co-designed wet-lab and bioinformatic workflow for metabarcoding bulk samples that removes both false-positive (tag jumps, chimeras, erroneous sequences) and false-negative (‘dropout’) errors. However, we find that it is not possible to recover relative-abundance information from amplicon data, due to persistent species-specific biases.
2. To present and validate our workflow, we created eight mock arthropod soups, all containing the same 248 arthropod morphospecies but differing in absolute and relative DNA concentrations, and we ran them under five different PCR conditions. Our pipeline includes qPCR-optimized PCR annealing temperature and cycle number, twin-tagging, multiple independent PCR replicates per sample, and negative and positive controls. In the bioinformatic portion, we introduce Begum, which is a new version of DAMe (Zepeda-Mendoza et al., 2016. BMC Res. Notes 9:255) that ignores heterogeneity spacers, allows primer mismatches when demultiplexing samples and is more efficient. Like DAMe, Begum removes tag-jumped reads and removes sequence errors by keeping only sequences that appear in more than one PCR above a minimum copy number per PCR. The filtering thresholds are user-configurable.
3. We report that OTU dropout frequency and taxonomic amplification bias are both reduced by using a PCR annealing temperature and cycle number on the low ends of the ranges currently used for the Leray-FolDegenRev primers. We also report that tag jumps and erroneous sequences can be nearly eliminated with Begum filtering, at the cost of only a small rise in dropouts. We replicate published findings that uneven size distribution of input biomasses leads to greater dropout frequency and that OTU size is a poor predictor of species input biomass. Finally, we find no evidence for ‘tag-biased’ PCR amplification.
4. To aid learning, reproducibility, and the design and testing of alternative metabarcoding pipelines, we provide our Illumina and input-species sequence datasets, scripts, a spreadsheet for designing primer tags and a tutorial.

中文翻译：

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Biodiversity Soup II：强调减少错误的批量样本元条形码管道

1. 尽管人们普遍认识到其在帮助环境管理决策方面的巨大潜力，但元条形码的应用需要改进，以减少 PCR 扩增、测序和文库生成过程中出现的多重错误。我们提出了一个共同设计的湿实验室和生物信息学工作流程，用于元条形码批量样本，可消除假阳性（标签跳跃、嵌合体、错误序列）和假阴性（“辍学”）错误。然而，我们发现由于持续的物种特异性偏差，不可能从扩增子数据中恢复相对丰度信息。
2. 为了展示和验证我们的工作流程，我们创建了八种模拟节肢动物汤，它们都包含相同的 248 种节肢动物形态，但绝对和相对 DNA 浓度不同，我们在五种不同的 PCR 条件下运行它们。我们的管道包括 qPCR 优化的 PCR 退火温度和循环数、双标记、每个样本的多个独立 PCR 重复以及阴性和阳性对照。在生物信息学部分，我们介绍了Begum，它是DAMe的新版本（Zepeda-Mendoza 等，2016. BMC Res. Notes 9:255），它忽略了异质性间隔，允许在解复用样本时引物错配，并且效率更高。像DAMe , Begum通过仅保留出现在多个 PCR 中的序列高于每个 PCR 的最小拷贝数，移除标签跳转读取并移除序列错误。过滤阈值是用户可配置的。
3. 我们报告说，通过在当前用于 Leray-FolDegenRev 引物的范围的低端使用 PCR 退火温度和循环数，OTU 丢失频率和分类学扩增偏差都降低了。我们还报告说，使用Begum过滤几乎可以消除标签跳转和错误序列，代价是丢失率仅略有增加。我们复制了已发表的研究结果，即输入生物量的大小分布不均匀会导致更大的辍学频率，并且 OTU 大小是物种输入生物量的不良预测指标。最后，我们没有发现“标签偏向”PCR 扩增的证据。
4. 为了帮助学习、可重复性以及替代元条形码管道的设计和测试，我们提供了 Illumina 和输入物种序列数据集、脚本、用于设计引物标签的电子表格和教程。