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      Phylogeny and historical biogeography analysis support Caucasian and Mediterranean centres of origin of key holoparasitic Orobancheae (Orobanchaceae) lineages

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          The extensive diversity of the tribe Orobancheae, the most species-rich lineage of holoparasitic Orobanchaceae , is concentrated in the Caucasus and Mediterranean regions of the Old World. This extant diversity has inspired hypotheses that these regions are also centres of origin of its key lineages, however the ability to test hypotheses has been limited by a lack of sampling and phylogenetic information about the species, especially in the Caucasus region. First, we assessed the phylogenetic relationships of several poorly known, problematic, or newly described species and host-races of four genera of Orobancheae occurring in the Caucasus region– Cistanche , Phelypaea , Phelipanche and Orobanche –using nuclear ribosomal (ITS) and plastid ( trnL–trnF) sequence data. Then we applied a probablistic dispersal-extinction-cladogenesis model of historical biogeography across a more inclusive clade of holoparasites, to explicitly test hypotheses of Orobancheae diversification and historical biogeography shifts. In sum, we sampled 548 sequences (including 196 newly generated) from 13 genera, 140 species, and 175 taxa across 44 countries. We find that the Western Asia (particularly the Caucasus) and the Mediterranean are the centre of origin for large clades of holoparasitic Orobancheae within the last 6 million years. In the Caucasus, the centres of diversity are composed both of long-branch taxa and shallow, recently diversified clades, while Orobancheae diversity in the Mediterranean appears to represent mainly recent diversification.

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          Most cited references 69

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org 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%.

                Author and article information

                Pensoft Publishers
                12 March 2021
                : 174
                : 165-194
                [1 ] Center for Research and Conservation of Biodiversity, Department of Environmental Biology, Institute of Biology, Jan Kochanowski University, Uniwersytecka 7, PL-25-406 Kielce, Poland Jan Kochanowski University Kielce Poland
                [2 ] Department of Biology and Health Sciences, Hendrix College, 1600 Washington Ave, Conway AR 72032, USA Department of Biology and Health Sciences, Hendrix College Conway United States of America
                [3 ] Department of Plant Cytology and Embryology, Institute of Botany, Jagiellonian University, Gronostajowa 9, PL-30-387 Kraków, Poland Jagiellonian University Kraków Poland
                [4 ] Grupo Botánico Cantábrico, ES-39722 Liérganes (Cantabria), Spain Grupo Botánico Cantábrico Liérganes Spain
                Author notes
                Corresponding author: Renata Piwowarczyk ( piwowarczyk@ 123456ujk.edu.pl )

                Academic editor: E. Fischer

                Renata Piwowarczyk, Adam C. Schneider, Grzegorz Góralski, Dagmara Kwolek, Magdalena Denysenko-Bennett, Anna Burda, Karolina Ruraż, Andrzej J. Joachimiak, Óscar Sánchez Pedraja

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Funded by: National Geographic Society 100006363 http://doi.org/10.13039/100006363
                Research Article
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