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      BOLDigger – a Python package to identify and organise sequences with the Barcode of Life Data systems

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      Metabarcoding and Metagenomics

      Pensoft Publishers

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          Abstract

          DNA metabarcoding workflows produce hundreds to ten-thousands of Operational Taxonomic Units (OTUs) or Exact Sequence Variants (ESVs) per analysis. In most workflows, a taxonomic assignment to these generated sequences is needed. This is typically done using publicly available databases. Especially, yet not exclusively, for Eumetazoan metabarcoding, the Barcode of Life Data system (BOLD) is the most comprehensive and curated reference barcode database and, therefore, typically the first choice for taxonomic assignment. While an application programme interface (API) exists to query data in large batches, no information on the many and important unpublished data are obtained through the API. The alternative approach using the BOLD identification engine on the website provides full access, yet it is restricted to 100 sequences at once. We developed a small platform-independent and graphical user interface (GUI) software package, BOLDigger, which aims to solve this problem by automating the process of sending successive requests of up to 100 sequences without surpassing the capacities of BOLD. BOLDigger can be used to download the results of the identification engine, as well as metadata for the obtained hits. For the selection of the best fitting hit, three different methods are implemented. A new approach, combining a threshold-based approach with the metadata information, was implemented to make use of the metadata.

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          Most cited references 5

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          Multiple Multilocus DNA Barcodes from the Plastid Genome Discriminate Plant Species Equally Well

          A universal barcode system for land plants would be a valuable resource, with potential utility in fields as diverse as ecology, floristics, law enforcement and industry. However, the application of plant barcoding has been constrained by a lack of consensus regarding the most variable and technically practical DNA region(s). We compared eight candidate plant barcoding regions from the plastome and one from the mitochondrial genome for how well they discriminated the monophyly of 92 species in 32 diverse genera of land plants (N = 251 samples). The plastid markers comprise portions of five coding (rpoB, rpoC1, rbcL, matK and 23S rDNA) and three non-coding (trnH-psbA, atpF–atpH, and psbK–psbI) loci. Our survey included several taxonomically complex groups, and in all cases we examined multiple populations and species. The regions differed in their ability to discriminate species, and in ease of retrieval, in terms of amplification and sequencing success. Single locus resolution ranged from 7% (23S rDNA) to 59% (trnH-psbA) of species with well-supported monophyly. Sequence recovery rates were related primarily to amplification success (85–100% for plastid loci), with matK requiring the greatest effort to achieve reasonable recovery (88% using 10 primer pairs). Several loci (matK, psbK–psbI, trnH-psbA) were problematic for generating fully bidirectional sequences. Setting aside technical issues related to amplification and sequencing, combining the more variable plastid markers provided clear benefits for resolving species, although with diminishing returns, as all combinations assessed using four to seven regions had only marginally different success rates (69–71%; values that were approached by several two- and three-region combinations). This performance plateau may indicate fundamental upper limits on the precision of species discrimination that is possible with DNA barcoding systems that include moderate numbers of plastid markers. Resolution to the contentious debate on plant barcoding should therefore involve increased attention to practical issues related to the ease of sequence recovery, global alignability, and marker redundancy in multilocus plant DNA barcoding systems.
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            Swarm v2: highly-scalable and high-resolution amplicon clustering

            Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.
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              Comparative performances of DNA barcoding across insect orders

              Background Previous studies on insect DNA barcoding provide contradictory results and suggest not consistent performances across orders. This work aims at providing a general evaluation of insect DNA barcoding and "mini-barcoding" by performing simulations on a large database of 15,948 DNA barcodes. We compared the proportions of correctly identified queries across a) six insect orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera and Orthoptera), b) four identification criteria (Best Match: BM; Best Close Match: BCM; All Species Barcodes: ASB; tree-based identification: NJT), and c) reference databases with different taxon coverage (100, 500, 1,000, 1,500 and 1,995 insect species). Results Analysis of variance revealed highly significant differences among ID criteria and insect orders. A posteriori comparisons of means showed that NJT had always a significantly lower identification success (NJT = 0.656, S.D. = 0.118) compared to both BM and BCM (BM = 0.948, S.D. = 0.026; BCM = 0.946, S.D. = 0.031). NJT showed significant variations among orders, with the highest proportion of correctly identified queries in Hymenoptera and Orthoptera and the lowest in Diptera. Conversely, the proportions of correct matches of BM and BCM were consistent across orders but a progressive increase in false identification was observed when larger reference databases were used. Conclusions Regardless the relatively low proportion of Type I errors (misidentification of queries which are represented in the reference database) of BM and BCM, the lack of reference DNA barcodes for 98% of the known insect species implies that insect DNA barcoding is heavily biased by Type II errors (misidentification of queries without conspecifics in the database). The detrimental effects of Type II errors could be circumvented if insect DNA barcoding is used to verify the lack of correspondence between a query and a list of properly referenced target species (e.g. insect pests). This "negative identification" would only be subjected to Type I errors and could be profitably adopted in insect quarantine procedures.
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                Author and article information

                Journal
                Metabarcoding and Metagenomics
                MBMG
                Pensoft Publishers
                2534-9708
                June 03 2020
                June 03 2020
                : 4
                Article
                10.3897/mbmg.4.53535
                © 2020

                http://creativecommons.org/licenses/by/4.0/

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