Corrigendum to: PEMA: a flexible pipeline for environmental DNA metabarcoding analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes

Abstract 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.

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Faculty Opinions recommendation of 18S ribosomal RNA and tetrapod phylogeny.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1014136.192862 ◽

2003 ◽

Author(s):

Elizabeth Kellogg

Keyword(s):

Ribosomal Rna ◽

18S Ribosomal Rna

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COMPARATIVE STUDY BETWEEN FISH COLLECTION BY NATIONAL CENSUS ON RIVER ENVIRONMENT AND ENVIRONMENTAL DNA METABARCODING

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.74.5_i_415 ◽

2018 ◽

Vol 74 (5) ◽

pp. I_415-I_420 ◽

Cited By ~ 1

Author(s):

Yoshihisa AKAMATSU ◽

Takayoshi TSUZUKI ◽

Ryota YOKOYAMA ◽

Yayoi FUNAHASHI ◽

Munehiro OHTA ◽

...

Keyword(s):

Comparative Study ◽

Environmental Dna ◽

Dna Metabarcoding ◽

Fish Collection

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Environmental DNA for functional diversity

10.1093/oso/9780198767220.003.0010 ◽

2018 ◽

Cited By ~ 1

Author(s):

Pierre Taberlet ◽

Aurélie Bonin ◽

Lucie Zinger ◽

Eric Coissac

Keyword(s):

Functional Groups ◽

Functional Diversity ◽

Environmental Dna ◽

Soil Organisms ◽

Meaningful Information ◽

Dna Metabarcoding ◽

Pros And Cons ◽

Target Community

Chapter 10 “Environmental DNA for functional diversity” discusses the potential of environmental DNA to assess functional diversity. It first focuses on DNA metabarcoding and discusses the extent to which this approach can be used and/or optimized to retrieve meaningful information on the functions of the target community. This knowledge usually involves coarsely defined functional groups (e.g., woody, leguminous, graminoid plants; shredders or decomposer soil organisms; pathogenicity or decomposition role of certain microorganisms). Chapter 10 then introduces metagenomics and metatranscriptomics approaches, their advantages, but also the challenges and solutions to appropriately sampling, sequencing these complex DNA/RNA populations. Chapter 10 finally presents several strategies and software to analyze metagenomes/metatranscriptomes, and discusses their pros and cons.

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Environmental DNA

10.1093/oso/9780198767220.001.0001 ◽

2018 ◽

Cited By ~ 137

Author(s):

Pierre Taberlet ◽

Aurélie Bonin ◽

Lucie Zinger ◽

Eric Coissac

Keyword(s):

Graduate Students ◽

Feeding Habits ◽

Environmental Dna ◽

Research Field ◽

Background Information ◽

Ecological Processes ◽

Biodiversity Research ◽

Dna Metabarcoding ◽

Standard Information

Environmental DNA (eDNA), i.e. DNA released in the environment by any living form, represents a formidable opportunity to gather high-throughput and standard information on the distribution or feeding habits of species. It has therefore great potential for applications in ecology and biodiversity management. However, this research field is fast-moving, involves different areas of expertise and currently lacks standard approaches, which calls for an up-to-date and comprehensive synthesis. Environmental DNA for biodiversity research and monitoring covers current methods based on eDNA, with a particular focus on “eDNA metabarcoding”. Intended for scientists and managers, it provides the background information to allow the design of sound experiments. It revisits all steps necessary to produce high-quality metabarcoding data such as sampling, metabarcode design, optimization of PCR and sequencing protocols, as well as analysis of large sequencing datasets. All these different steps are presented by discussing the potential and current challenges of eDNA-based approaches to infer parameters on biodiversity or ecological processes. The last chapters of this book review how DNA metabarcoding has been used so far to unravel novel patterns of diversity in space and time, to detect particular species, and to answer new ecological questions in various ecosystems and for various organisms. Environmental DNA for biodiversity research and monitoring constitutes an essential reading for all graduate students, researchers and practitioners who do not have a strong background in molecular genetics and who are willing to use eDNA approaches in ecology and biomonitoring.

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Estimating aquatic plant diversity and distribution in rivers from Jingjinji Region, China, using environmental DNA metabarcoding and a traditional survey method

Environmental Research ◽

10.1016/j.envres.2021.111348 ◽

2021 ◽

pp. 111348

Author(s):

Fenfen Ji ◽

Liang Yan ◽

Saihong Yan ◽

Tianlong Qin ◽

Jianzhong Shen ◽

...

Keyword(s):

Plant Diversity ◽

Aquatic Plant ◽

Environmental Dna ◽

Survey Method ◽

Dna Metabarcoding

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Molecular Identification of Trypanosoma evansi Isolated from Arabian Camels (Camelus dromedarius) in Riyadh and Al-Qassim, Saudi Arabia

Animals ◽

10.3390/ani11041149 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1149

Author(s):

Dina M. Metwally ◽

Isra M. Al-Turaiki ◽

Najwa Altwaijry ◽

Samia Q. Alghamdi ◽

Abdullah D. Alanazi

Keyword(s):

Saudi Arabia ◽

Infection Rate ◽

Ribosomal Rna ◽

Phylogenetic Analyses ◽

Trypanosoma Evansi ◽

Camelus Dromedarius ◽

18S Ribosomal Rna ◽

18S Ribosomal Rna Gene ◽

Polymerase Chain ◽

Pcr Targeting

We analyzed the blood from 400 one-humped camels, Camelus dromedarius (C. dromedarius), in Riyadh and Al-Qassim, Saudi Arabia to determine if they were infected with the parasite Trypanosoma spp. Polymerase chain reaction (PCR) targeting the internal transcribed spacer 1 (ITS1) gene was used to detect the prevalence of Trypanosoma spp. in the camels. Trypanosoma evansi (T. evansi) was detected in 79 of 200 camels in Riyadh, an infection rate of 39.5%, and in 92 of 200 camels in Al-Qassim, an infection rate of 46%. Sequence and phylogenetic analyses revealed that the isolated T. evansi was closely related to the T. evansi that was detected in C. dromedarius in Egypt and the T. evansi strain B15.1 18S ribosomal RNA gene identified from buffalo in Thailand. A BLAST search revealed that the sequences are also similar to those of T. evansi from beef cattle in Thailand and to T. brucei B8/18 18S ribosomal RNA from pigs in Nigeria.

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