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      Extraction kits could affect the interpretation of metabarcoding results of sediment samples taken around salmon farms

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      ARPHA Conference Abstracts

      Pensoft Publishers

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          Abstract

          Differences in PCR strategies and errors, sequencing errors and methods used for extractions affect sequence data and potentially its interpretation. These effects could vary based on the target fragments, which are also influenced by limitations of incomplete databases. In this study, we tested the effects of two different proprietary DNA extraction kits on sediment samples, for the purposes of benthic monitoring of salmon farms. The levels of organic enrichment at farms show a gradient from cage edge to more distant locations. The effects of enrichment on benthic communities can be established with metabarcoding.We collected samples at three salmon farms in Scotland, at varying distances from cage edge. The sediments underneath two of the farms was fine while the sediment under the other was coarse, with a larger mean particle size.We extracted the samples with two different kits, each using a different mass of sediment – Qiagen DNeasy PowerSoil Pro (0.5 g) and Qiagen DNeasy PowerMax (5 g). We then subjected each extract to three independent PCRs targeting 16S (bacterial) and CO1 (eukaryotic) fragments. The PCR products of samples and blanks were sequenced with an Illumina MiSeq instrument on a single run.We denoised the sequenced data using DADA2 and rarefied it before analysis (Callahan et al. 2016). The 16S data was annotated against the seven-level SILVA database (Quast 2012). We collated this data at ‘Family’ level. The CO1 data was filtered to remove Amplicon Sequence Variants (ASVs) present in only one sample and ASVs with a frequency of less than ten reads across all samples. The read count data of family level 16S and ASVs of CO1 were transformed and converted to Bray-Curtis dissimilarity matrices (Bray and Curtis 1957). A permutational multivariate analysis of variance was carried out. We also ordinated these data with non-metric multi-dimensional scaling.474 bacterial families and 3380 eukaryotic ASVs were included in the analysis. The samples extracted with both kits demonstrated a gradient based on distance from cage edge. This gradient in sampling stations was observed with both the 16S and CO1 markers. Data from both markers and kits showed a greater distinction between cage edge stations and more distant stations in the farms characterised by fine sediment. The two extraction kits showed similar trends but differed in their results.The 16S data showed a separation of samples by extraction kit along the y-axis. PowerMax extractions were associated with higher values on the y-axis (Fig. 1). The multivariate analysis of variance of the 16S data showed that extraction kit contributes to approximately 7% (p<0.001) of variation in data.The CO1 ASV data also showed a grouping of samples of both kits along the x-axis on the basis of distance from the farm (Fig. 1). The CO1 data showed that extraction kits contribute to about 5% (p<0.001) of the variation. The results of the two extraction kits were more similar to each other with the CO1 marker than with 16S. The greater axes values and grouping in the CO1 ordination, indicate that it is able to split farms and distances better than 16S.We show that both extraction kits demonstrated a gradient according to distance from the cage edge. However, there was a systematic difference between the extraction kits. Variability due to kit was greater with the 16S marker despite it including fewer bacterial families than CO1 ASVs. We recommend that the same extraction kit be used to develop protocols for monitoring of fish farms with metabarcoding. Though both kits demonstrate the same major trend, subtle differences may not be distinguished. These variations between the kits could influence the results and interpretation of metabarcoding.

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          Most cited references 3

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            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.
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              An Ordination of the Upland Forest Communities of Southern Wisconsin

               J. Bray,  J. Curtis (1957)
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                Author and article information

                Journal
                ARPHA Conference Abstracts
                ACA
                Pensoft Publishers
                2603-3925
                March 04 2021
                March 04 2021
                : 4
                Article
                10.3897/aca.4.e64830
                © 2021

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