基于Nextflow构建的宏条形码自动化分析流程EPPS

doi:10.17520/biods.2018211

摘要/Abstract

摘要：

基于宏条形码技术的物种快速检测有助于生物多样性的评估、预测和保护。本文介绍了常用宏条形码分析的步骤和参数设定方法。我们利用Nextflow搭建了一款宏条形码分析流程EPPS, 可以自动化地运行从原始数据的质量控制到环境多样性的比较。Nextflow软件还拥有流程监控的功能, 可视化输出每个进程所消耗的时间与内存。本文还使用测试数据和已发表数据证明该平台能够有效地分析宏条形码数据并可靠地分析环境生物多样性的相似性。

关键词: 环境DNA, USEARCH, Trimmomatic, 主成分分析

Abstract

Metabarcoding helps to quickly assess biodiversity. In this study, we discuss popular metabarcoding analytical tools and parameter settings. We also develop a metabarcoding bioinformatics pipeline, EPPS, to process data from quality control of raw reads to biodiversity comparisons between samples using a pipeline building program, Nextflow. The EPPS pipeline can summarize the time and memory cost of each process in the pipeline. We also apply the pipeline on a test dataset and a public dataset from a previous study. The result suggests that this pipeline can reliably analyze metabarcoding data and facilitate pipeline sharing of metabarcoding studies.

Key words: environmental DNA, USEARCH, Trimmomatic, principal component analysis

李诣远, DavidC.Molik, MichaelE.Pfrender (2019) 基于Nextflow构建的宏条形码自动化分析流程EPPS. 生物多样性, 27, 567-575. DOI: 10.17520/biods.2018211.

Li Yiyuan, C. Molik David, E. Pfrender Michael (2019) EPPS, a metabarcoding bioinformatics pipeline using Nextflow. Biodiversity Science, 27, 567-575. DOI: 10.17520/biods.2018211.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2018211

https://www.biodiversity-science.net/CN/Y2019/V27/I5/567

图/表 4

图1 EPPS的主要分析步骤。OTU聚类分析还包括去除重复序列、OTU聚类和嵌合体的检测。

Fig. 1 The workflow of EPPS. OTU clustering step includes, dereplication, OTU clustering and chimera detection.

图2 EPPS流程每一个进程的时间消耗。横坐标代表时间, 单位是秒。最左列的名称分别对应了宏条形码分析的流程。filter: 测序质量控制; demultiplex: PCR引物的删除, 如果有多个引物则将各个引物分开; merge: 合并正向和反向序列; otu_clustering/map: OTU聚类分析; plot: 主成分分析。由于测试数据有4个样品, 因此每个进程的右侧括号里有1-4的序号。浅色进度条代表进程所消耗的系统时间。深色进度条代表的是每个进程的CPU时间。每个进度条包含有两个数字, 第1个数字代表每个进度的系统时间, 第2个数字代表虚拟内存的峰值。

Fig. 2 The timeline chart of EPPS. The x-axis is the amount of time for each process in seconds. Each row indicates the name of different stages of the analysis. filter, Data filtering; demultiplex, Primer removal and demultiplex if there are multiple primers; merge, Merging of forward and reverse reads; otu_clustering and map, OTU clustering and mapping of reads; plot, Plotting PCA plot. As there are four samples in the testing data set, there are four processes (1 to 4) for filter, demultiplex, merge, and map steps. Each bar indicates the time for each process. The dark area in each bar represents the real execution time. Each bar displays two numbers: the task duration time and the virtual memory size peak.

图3 基于测试数据的主成分分析结果。图中每一个点代表一个测试数据的样品。点与点之间距离越近代表样品之间的物种组成相似度越高。例如, test3和test4的相似度大于test3和test1的相似度。

Fig. 3 PCA plot based on testing data. Each dot in the figure represents a test sample. The distance between dots indicates the dissimilarity between samples. For example, the similarity between test3 and test4 is higher than test1 and test2.

图4 公共数据的分析结果。样品的名称编号1-8分别代表从最上游到下游的8个采样点。编号的后缀a, b, c分别代表同一个采样地点的3次独立的重复取样。基于图中的结果, 最上游的样品Location1有独特的鱼类多样性组成。Location 3-6有类似的鱼类多样性组成。最下游的样品Location 7-8有类似的鱼类多样性组成。

Fig. 4 EPPS result based on public data set. Samples are named from 1 to 8 from upstream to downstream. The suffix “a”, “b” and “c” indicate three replicates of the same sampling location. Based on the PCA, the most upstream sample (Location 1) has unique fish composition. Location 3-6 have similar fish composition. The downstream samples (Location 7-8) share similar fish composition.

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