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      Comparing Sanger sequencing and high-throughput metabarcoding for inferring photobiont diversity in lichens

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          Abstract

          The implementation of HTS (high-throughput sequencing) approaches is rapidly changing our understanding of the lichen symbiosis, by uncovering high bacterial and fungal diversity, which is often host-specific. Recently, HTS methods revealed the presence of multiple photobionts inside a single thallus in several lichen species. This differs from Sanger technology, which typically yields a single, unambiguous algal sequence per individual. Here we compared HTS and Sanger methods for estimating the diversity of green algal symbionts within lichen thalli using 240 lichen individuals belonging to two species of lichen-forming fungi. According to HTS data, Sanger technology consistently yielded the most abundant photobiont sequence in the sample. However, if the second most abundant photobiont exceeded 30% of the total HTS reads in a sample, Sanger sequencing generally failed. Our results suggest that most lichen individuals in the two analyzed species, Lasallia hispanica and L. pustulata, indeed contain a single, predominant green algal photobiont. We conclude that Sanger sequencing is a valid approach to detect the dominant photobionts in lichen individuals and populations. We discuss which research areas in lichen ecology and evolution will continue to benefit from Sanger sequencing, and which areas will profit from HTS approaches to assessing symbiont diversity.

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          Benefits of plant diversity to ecosystems: immediate, filter and founder effects

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            Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data

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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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                Author and article information

                Contributors
                imke.schmitt@senckenberg.de
                francesco.dalgrande@senckenberg.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 June 2018
                5 June 2018
                2018
                : 8
                : 8624
                Affiliations
                [1 ]Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325 Frankfurt am Main, Germany
                [2 ]ISNI 0000 0004 1936 9721, GRID grid.7839.5, Institute of Ecology, Evolution and Diversity, , Goethe University Frankfurt am Main, ; Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany
                [3 ]ISNI 0000 0001 2157 7667, GRID grid.4795.f, Departamento de Farmacología, Farmacognosia y Botánica, , Universidad Complutense de Madrid, ; 28040 Madrid, Spain
                Author information
                http://orcid.org/0000-0002-0955-374X
                Article
                26947
                10.1038/s41598-018-26947-8
                5988838
                29872090
                bf4d9eca-a138-48e5-b1ba-900e4ed025b5
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 March 2018
                : 22 May 2018
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