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      Single-cell genomics of a bloom-forming phytoplankton species reveals population genetic structure across continents

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          Abstract

          The study of microbial diversity over time and space is fundamental to the understanding of their ecology and evolution. The underlying processes driving these patterns are not fully resolved but can be studied using population genetic approaches. Here we investigated the population genetic structure of Gonyostomum semen, a bloom-forming phytoplankton species, across two continents. The species appears to be expanding in Europe, whereas similar trends are not observed in the USA. Our aim was to investigate if populations of Gonyostomum semen in Europe and in the USA are genetically differentiated, if there is population genetic structure within the continents, and what the potential drivers of differentiation are. To this end, we used a novel method based on single-amplified genomes combined with Restriction-site Associated DNA sequencing that allows de novo genotyping of natural single-cell isolates without the need for culturing. We amplified over 900 single-cell genomes from 25 lake populations across Europe and the USA and identified two distinct population clusters, one in Europe and another in the USA. Low genetic diversity in European populations supports the hypothesized recent expansion of Gonyostomum semen on this continent. Geographic population structure within each continent was associated with differences in environmental variables that may have led to ecological divergence of population clusters. Overall, our results show that single-amplified genomes combined with Restriction-site Associated DNA sequencing can be used to analyze microalgal population structure and differentiation based on single-cell isolates from natural, uncultured samples.

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          BLAST+: architecture and applications

          Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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              Detecting the number of clusters of individuals using the software structure: a simulation study

              The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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                Author and article information

                Contributors
                Journal
                ISME J
                ISME J
                ismej
                The ISME Journal
                Oxford University Press
                1751-7362
                1751-7370
                January 2024
                15 March 2024
                15 March 2024
                : 18
                : 1
                : wrae045
                Affiliations
                Department of Biology , Aquatic Ecology, Lund University , 22362 Lund, Sweden
                Department of Earth Sciences, University of Oxford , Oxford OX1 3AN, UK
                National Bioinformatics Infrastructure Sweden (NBIS) , SciLifeLab, Department of Biology, Lund University , 22362 Lund, Sweden
                Department of Biology , Aquatic Ecology, Lund University , 22362 Lund, Sweden
                Author notes
                Corresponding author: Raphael Gollnisch, Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, United Kingdom. Email: raphael.gollnisch@ 123456gmail.com
                Article
                wrae045
                10.1093/ismejo/wrae045
                11065318
                38489771
                94a90abb-c4ba-4e9a-9700-eebf0fd6d234
                © The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Microbial Ecology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 September 2023
                : 30 December 2023
                : 26 April 2024
                Page count
                Pages: 13
                Funding
                Funded by: SINGEK;
                Funded by: Crafoord Foundation;
                Award ID: 20170722
                Funded by: Botanical Society Lund;
                Funded by: Ove Almborn Donation Fund;
                Funded by: Royal Physiographic Society of Lund;
                Funded by: University of Michigan Biological Station Fellowship;
                Categories
                Original Article
                AcademicSubjects/SCI00010
                AcademicSubjects/SCI00960
                AcademicSubjects/SCI01150
                AcademicSubjects/SCI02281

                Microbiology & Virology
                restriction-site associated dna (rad) sequencing,single-cell whole-genome amplification (wga),gonyostomum semen,dispersal,adaptation,ecological divergence

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