Data from: A from-benchtop-to-desktop workflow for validating HTS data and for taxonomic identification in diet metabarcoding studies

The main objective of this work was to develop and validate a robust and reliable ‘from benchtop-to-desktop’ metabarcoding workflow to investigate the diet of invertebrate-eaters. We applied our workflow to fecal DNA samples of an invertebrate-eating fish species. A fragment of the COI gene was amplified by combining two minibarcoding primer sets to maximize the taxonomic coverage. Amplicons were sequenced by an Illumina MiSeq platform. We developed a filtering approach based on a series of non-arbitrary thresholds established from control samples and from molecular replicates in order to address the elimination of cross-contamination, PCR/sequencing errors and mistagging artifacts. This resulted in a conservative and informative metabarcoding dataset. We developed a taxonomic assignment procedure that combines different approaches and that allowed the identification of ~75% of invertebrate COI variants to the species level. Moreover, based on the diversity of the variants, we introduced a semi-quantitative statistic in our diet study, the Minimum Number of Individuals (MNI), which is based on the number of distinct variants in each sample. The metabarcoding approach described in this paper may guide future diet studies that aim to produce robust datasets associated with a fine and accurate identification of prey items.本研究的核心目标为开发并验证一套稳定可靠的「从实验台到桌面（benchtop-to-desktop）」宏条形码（metabarcoding）工作流，以探究食无脊椎动物类群的食性。我们将该工作流应用于一种食无脊椎动物鱼类的粪便DNA样本。通过组合两套微型条形码（minibarcoding）引物扩增COI基因片段，以最大化分类学覆盖范围。扩增子（amplicon）采用Illumina MiSeq测序平台进行测序。为消除交叉污染、PCR/测序错误及标签错配伪影（mistagging artifacts），我们基于对照样本与分子重复样本建立了一系列非任意阈值，并据此开发了一套过滤方法，最终得到了一套严谨且信息丰富的宏条形码数据集。我们还开发了一套结合多种分析方法的分类学鉴定流程，可将约75%的无脊椎动物COI基因变异序列鉴定至物种水平。此外，基于变异序列的多样性，我们在食性研究中引入了半定量统计指标——最小个体数（Minimum Number of Individuals, MNI），该指标以每个样本中的独特变异序列数量为计算依据。本研究所述的宏条形码方法，可为未来旨在构建稳定可靠数据集、并实现猎物类群精细精准鉴定的食性研究提供参考与指导。