Biodiversity Soup II: A bulk‐sample metabarcoding pipeline emphasizing error reduction. Issue 7 (1st May 2021)

• Record Type: Journal Article
• Authors: Yang, Chunyan; Bohmann, Kristine; Wang, Xiaoyang; Cai, Wang; Wales, Nathan; Ding, Zhaoli; Gopalakrishnan, Shyam; Yu, Douglas W.
• Is Part Of: Methods in ecology and evolution. Volume 12:Issue 7(2021)
• Page Start: 1252
• Page End: 1264
• Publication Date: 2021-05-01
• Subjects: bulk‐sample DNA metabarcoding -- environmental DNA -- environmental impact assessment -- false negatives -- false positives -- Illumina high‐throughput sequencing -- tag bias; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13602
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: 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. 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. 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. 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. 摘要: 高通量条形码在环境保护和管理相关决策研究中的适用性已获得广泛的共识, 但是若要在更广的领域应用该方法, 还需要进一步减少PCR扩增、文库构建以及测序所带来的错误和偏好。本研究设计了一种针对混合生物样本的高通量条形码流程, 这一流程改进了高通量条形码的实验设计和生物信息学分析, 能减少结果中的假阳性 (如标签错配、嵌合体、错误序列) 和假阴性。研究结果还显示, 由于不同物种的扩增效率存在差异, 因此基于扩增子数据的高通量条形码并不能获取准确的物种定量信息。 本研究人为构建了8个生物多样性汤——节肢动物的混合样本, 每一个汤中都含有同样的248个节肢动物的形态种, 它们的DNA被以不同的浓度比例混入这8个汤中。然后这8个汤在5种不同的PCR条件下被扩增。本研究采用双胞胎标记法来双向标记引物, 每个样本都进行多次独立的PCR扩增, 每一批次的PCR扩增都包含阳性与阴性对照, 并且我们通过qPCR来优化高通量条形码PCR最终使用的退火温度和循环数。在生物信息学分析部分, 我们采用的是Begum (Zepeda Mendoza等发表的DAMe的改进版, 2016. BMCRes. Notes 9:255), 它在样本拆分时可以忽略为了测序中平衡碱基而添加在标签上的几个碱基, 并允许引物序列的错配, 且提高了运算速率。Begum能去除由于标签跳动所产生的假阳性序列, 以及通过设置多个PCR重复和某一序列的重复出现次数来过滤PCR和测序等产生的错误序列。 本研究结果显示, 在使用Leray‐Fol‐Degen‐Rev的引物对扩增时, 采取较低的退火温度和循环数能有效提高OTU得率, 及减少扩增偏好性。本研究还发现Begum能过滤掉几乎所有的标签跳动和错误序列。我们再次证实了样本中物种生物量的不均衡会导致结果中更多的物种信息丢失, 而且OTU的序列数并不能直接用于指示物种的生物量。最后, 研究结果表明在优化的PCR条件下, 并未产生明显的标签偏好现象 (即由连接引物的标签不同而引发的PCR扩增偏好) 。 为了让更多人能学习、验证、并设计适合自己的高通量条形码流程, 我们提供了本文中用于构建生物多样性汤所有物种的序列、Illumina测序数据、完整的分析命令脚本, 以及用于引物标签设计的表格和指南。