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用于验证高通量测序数据和进行饮食代谢组学研究中的分类鉴定的从实验台到桌面的工作流程。

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

机构信息

Aix Marseille Univ, Avignon Univ, CNRS, IRD, UMR IMBE, Marseille, France.

Irstea, UR RECOVER, Equipe FRESCHCO, Aix-en-Provence, France.

出版信息

Mol Ecol Resour. 2017 Nov;17(6):e146-e159. doi: 10.1111/1755-0998.12703. Epub 2017 Sep 20.

Abstract

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 faecal DNA samples of an invertebrate-eating fish species. A fragment of the cytochrome c oxidase I (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 nonarbitrary thresholds established from control samples and from molecular replicates to address the elimination of cross-contamination, PCR/sequencing errors and mistagging artefacts. This resulted in a conservative and informative metabarcoding data set. 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 semiquantitative statistic in our diet study, the minimum number of individuals, which is based on the number of distinct variants in each sample. The metabarcoding approach described in this article may guide future diet studies that aim to produce robust data sets associated with a fine and accurate identification of prey items.

摘要

这项工作的主要目的是开发和验证一种稳健可靠的“从实验台到桌面”的宏条形码工作流程,以研究食虫无脊椎动物的饮食。我们将我们的工作流程应用于食虫鱼类的粪便 DNA 样本。通过结合两个 minibarcoding 引物对来扩增细胞色素 c 氧化酶 I(COI)基因片段,以最大限度地提高分类覆盖范围。通过 Illumina MiSeq 平台对扩增子进行测序。我们开发了一种过滤方法,该方法基于从对照样品和分子重复中建立的一系列非任意阈值,以解决消除交叉污染、PCR/测序错误和误标记人工制品的问题。这导致了一个保守和信息丰富的宏条形码数据集。我们开发了一种分类分配程序,该程序结合了不同的方法,并允许将约 75%的无脊椎动物 COI 变体识别到种水平。此外,基于变体的多样性,我们在饮食研究中引入了一种半定量统计数据,即最小个体数,它基于每个样本中不同变体的数量。本文描述的宏条形码方法可以指导未来的饮食研究,旨在生成与猎物精细准确识别相关的稳健数据集。

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