ArticleGigaScience2020
PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 49 citations in OpenAlex.
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- A long-term ecological research dataset from the marine genetic monitoring programme ARMS-MBON 2020-2021.Biodiversity data journal · 2025Article
- Visualizing metagenomic and metatranscriptomic data: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses.Molecular ecology resources · 2024Review
- The gut microbiota intervenes in glucose tolerance and inflammation by regulating the biosynthesis of taurodeoxycholic acid and carnosine.Frontiers in cellular and infection microbiology · 2024Article
- VTAM: A robust pipeline for validating metabarcoding data using controls.Computational and structural biotechnology journal · 2023Article
- Tourmaline: A containerized workflow for rapid and iterable amplicon sequence analysis using QIIME 2 and Snakemake.GigaScience · 2022Article
- PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types.Microorganisms · 2022Article
- 0s and 1s in marine molecular research: a regional HPC perspective.GigaScience · 2021Article
- An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies.Biomolecules · 2021Review
- DNA Metabarcoding for the Characterization of Terrestrial Microbiota-Pitfalls and Solutions.Microorganisms · 2021Review
- Reprocessing 16S rRNA Gene Amplicon Sequencing Studies: (Meta)Data Issues, Robustness, and Reproducibility.Frontiers in cellular and infection microbiology · 2021Article
- Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology.GigaScience · 2020Article
- PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes.GigaScience · 2020Article
- CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis.PloS one · 2020Article
- Article
- PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom.Frontiers in microbiology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEnvironmental 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.
findingsPEMA 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.
conclusionsA 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.