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      Extensive introgression and mosaic genomes of Mediterranean endemic lizards

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          Abstract

          The Mediterranean basin is a hotspot of biodiversity, fuelled by climatic oscillation and geological change over the past 20 million years. Wall lizards of the genus Podarcis are among the most abundant, diverse, and conspicuous Mediterranean fauna. Here, we unravel the remarkably entangled evolutionary history of wall lizards by sequencing genomes of 34 major lineages covering 26 species. We demonstrate an early (>11 MYA) separation into two clades centred on the Iberian and Balkan Peninsulas, and two clades of Mediterranean island endemics. Diversification within these clades was pronounced between 6.5–4.0 MYA, a period spanning the Messinian Salinity Crisis, during which the Mediterranean Sea nearly dried up before rapidly refilling. However, genetic exchange between lineages has been a pervasive feature throughout the entire history of wall lizards. This has resulted in a highly reticulated pattern of evolution across the group, characterised by mosaic genomes with major contributions from two or more parental taxa. These hybrid lineages gave rise to several of the extant species that are endemic to Mediterranean islands. The mosaic genomes of island endemics may have promoted their extraordinary adaptability and striking diversity in body size, shape and colouration, which have puzzled biologists for centuries.

          Abstract

          Islands can provide insights into the evolution of diverse adaptations. The genomes of 34 major lineages of Mediterranean wall lizards reveal a highly reticulated pattern of evolution across the group, characterised by mosaic genomes and showing that hybrid lineages gave rise to several extant endemics.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Contributors
                weizhao.yang@biol.lu.se
                tobias.uller@biol.lu.se
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 May 2021
                12 May 2021
                2021
                : 12
                : 2762
                Affiliations
                [1 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Department of Biology, , Lund University, ; Lund, Sweden
                [2 ]GRID grid.5808.5, ISNI 0000 0001 1503 7226, CIBIO/InBIO Research Centre in Biodiversity and Genetic Resources, , University of Porto, Campus Agrário de Vairão, ; Vairão, Portugal
                [3 ]GRID grid.1009.8, ISNI 0000 0004 1936 826X, School of Natural Sciences, , University of Tasmania, Sandy Bay, ; Tasmania, Australia
                [4 ]GRID grid.158820.6, ISNI 0000 0004 1757 2611, Department of Health, Life and Environmental Sciences, , University of L’Aquila, ; Coppito, L’Aquila Italy
                Author information
                http://orcid.org/0000-0001-5466-2729
                http://orcid.org/0000-0003-4648-6950
                http://orcid.org/0000-0001-5150-2850
                http://orcid.org/0000-0002-7897-7204
                http://orcid.org/0000-0002-3804-2690
                http://orcid.org/0000-0003-1293-5842
                Article
                22949
                10.1038/s41467-021-22949-9
                8114931
                33980851
                c2c7be9f-669b-484b-9682-766ae09b74dc
                © The Author(s) 2021

                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
                : 28 October 2020
                : 28 March 2021
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                © The Author(s) 2021

                Uncategorized
                evolutionary genetics,phylogenetics,herpetology
                Uncategorized
                evolutionary genetics, phylogenetics, herpetology

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