2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Development of droplet digital Polymerase Chain Reaction assays for the detection of long-finned ( Anguilla dieffenbachii) and short-finned ( Anguilla australis) eels in environmental samples

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Freshwater eels are ecologically, and culturally important worldwide. The New Zealand long-finned eel ( Anguilla dieffenbachii) and short-finned eel ( Anguilla australis) are apex predators, playing an important role in ecosystem functioning of rivers and lakes. Recently, there has been a national decline in their populations due to habitat destruction and commercial harvest. The emergence of targeted environmental DNA detection methodologies provides an opportunity to enhance information about their past and present distributions. In this study we successfully developed species-specific droplet digital Polymerase Chain Reaction (ddPCR) assays to detect A. dieffenbachii and A. australis DNA in water and sediment samples. Assays utilized primers and probes designed for regions of the mitochondrial cytochrome b and 16S ribosomal RNA genes in A. dieffenbachii and A. australis, respectively. River water samples ( n = 27) were analyzed using metabarcoding of fish taxa and were compared with the ddPCR assays. The presence of A. dieffenbachii and A. australis DNA was detected in a greater number of water samples using ddPCR in comparison to metabarcoding. There was a strong and positive correlation between gene copies (ddPCR analyses) and relative eel sequence reads (metabarcoding analyses) when compared to eel biomass. These ddPCR assays provide a new method for assessing spatial distributions of A. dieffenbachii and A. australis in a range of environments and sample types.

          Related collections

          Most cited references110

          • Record: found
          • Abstract: found
          • Article: not found

          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
            • Record: found
            • Abstract: not found
            • Article: not found

            Cutadapt removes adapter sequences from high-throughput sequencing reads

              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                27 September 2021
                2021
                : 9
                : e12157
                Affiliations
                [1 ]Cawthron Institute , Nelson, New Zealand
                [2 ]Victoria University of Wellington , Wellington, New Zealand
                [3 ]Institute of Marine Science, University of Auckland , Warkworth, New Zealand
                [4 ]GNS Science , Lower Hutt, New Zealand
                Article
                12157
                10.7717/peerj.12157
                8483004
                34692247
                dc64acea-272d-4e1b-a220-2df9e2598ea8
                ©2021 Thomson-Laing et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 19 May 2021
                : 24 August 2021
                Funding
                Funded by: New Zealand Ministry of Business, Innovation and Employment Research Programme
                Award ID: C05X1707
                Funded by: Envirolink Tools
                Award ID: CAWX1802
                This research was funded by the New Zealand Ministry of Business, Innovation and Employment research programme - Our lakes’ health; past, present, and future (C05X1707). The authors acknowledge Envirolink funding (Envirolink Tools grant CAWX1802) for covering the HTS costs associated with the environmental water samples and for providing these samples for our use. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Aquaculture, Fisheries and Fish Science
                Ecosystem Science
                Molecular Biology
                Zoology
                Freshwater Biology

                environmental dna,edna,metabarcoding,high-throughput sequencing,anguilla,droplet digital pcr

                Comments

                Comment on this article

                Related Documents Log