BACKGROUND Environmental DNA and metabarcoding allow the identification of a mixture of species and launch a new era in bio- and eco-assessment. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available; each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy. Adding to this complexity, the computation capacity of high-performance computing systems is frequently required for such analyses. To address the difficulties, bioinformatic pipelines need to combine state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune each study. Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise programming languages specialized for big data pipelines incorporate features like roll-back checkpoints and on-demand partial pipeline execution. FINDINGS PEMA is a containerized assembly of key metabarcoding analysis tools that requires low effort in setting up, running, and customizing to researchers' needs. Based on third-party tools, PEMA performs read pre-processing, (molecular) operational taxonomic unit clustering, amplicon sequence variant inference, and taxonomy assignment for 16S and 18S ribosomal RNA, as well as ITS and COI marker gene data. Owing to its simplified parameterization and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against both mock communities and previously published datasets and achieved results of comparable quality. CONCLUSIONS A high-performance computing-based approach was used to develop PEMA; however, it can be used in personal computers as well. PEMA's time-efficient performance and good results will allow it to be used for accurate environmental DNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

中文翻译：

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PEMA：用于 16S/18S 核糖体 RNA、ITS 和 COI 标记基因环境 DNA 元条形码分析的灵活管道。

背景技术环境DNA和元条形码允许识别物种混合物并开启生物和生态评估的新时代。从原始数据中获取分类分配的矩阵需要许多步骤。对于其中的大多数，有大量的工具可供使用；每个工具的执行参数都需要进行定制，以反映每个实验的特性。此类分析经常需要高性能计算系统的计算能力，这增加了这种复杂性。为了解决这些困难，生物信息学管道需要将最先进的技术和算法与易于设置使用的框架相结合，以便研究人员能够调整每项研究。软件容器化技术简化了跨操作系统的软件包共享和运行；因此，它们极大地促进了管道的开发和使用。同样，专门用于大数据管道的编程语言也包含回滚检查点和按需部分管道执行等功能。研究结果 PEMA 是关键元条形码分析工具的容器化组件，只需很少的工作量即可根据研究人员的需求进行设置、运行和定制。基于第三方工具，PEMA 对 16S 和 18S 核糖体 RNA 以及 ITS 和 COI 标记基因数据执行读取预处理、（分子）操作分类单元聚类、扩增子序列变异推断和分类分配。由于其简化的参数化和检查点支持，PEMA 允许用户探索管道特定步骤的替代算法，而无需完全重新执行。PEMA 针对模拟社区和之前发布的数据集进行了评估，并取得了相当质量的结果。结论 采用基于高性能计算的方法来开发 PEMA；然而，它也可以用于个人计算机。PEMA的省时性能和良好结果将使其能够用于准确的环境DNA元条形码分析，从而增强下一代生物多样性评估研究的适用性。