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      Extreme genome scrambling in marine planktonic Oikopleura dioica cryptic species

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          Abstract

          Genome structural variations within species are rare. How selective constraints preserve gene order and chromosome structure is a central question in evolutionary biology that remains unsolved. Our sequencing of several genomes of the appendicularian tunicate Oikopleura dioica around the globe reveals extreme genome scrambling caused by thousands of chromosomal rearrangements, although showing no obvious morphological differences between these animals. The breakpoint accumulation rate is an order of magnitude higher than in ascidian tunicates, nematodes, Drosophila, or mammals. Chromosome arms and sex-specific regions appear to be the primary unit of macrosynteny conservation. At the microsyntenic level, scrambling did not preserve operon structures, suggesting an absence of selective pressure to maintain them. The uncoupling of the genome scrambling with morphological conservation in O. dioica suggests the presence of previously unnoticed cryptic species and provides a new biological system that challenges our previous vision of speciation in which similar animals always share similar genome structures.

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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%.
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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                March 2024
                March 2024
                : 34
                : 3
                : 426-440
                Affiliations
                [1 ]Genomics and Regulatory Systems Unit, Okinawa Institute of Science and Technology Graduate University (OIST), Onna-son, Okinawa 904-0495, Japan;
                [2 ]Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona (UB), Barcelona 08028, Spain;
                [3 ]Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona (UB), Barcelona 08028, Spain;
                [4 ]Centre of Plant Structural and Functional Genomics, Institute of Experimental Botany, 779 00 Olomouc, Czech Republic;
                [5 ]Sars International Centre, University of Bergen, Bergen N-5008, Norway;
                [6 ]Department of Biological Sciences, University of Bergen, Bergen N-5020, Norway;
                [7 ]Faculty of Science, Kagoshima University, Kagoshima 890-0065, Japan;
                [8 ]Department of Biological Sciences, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan
                Author notes
                [9]

                These authors contributed equally to this work.

                Present addresses: 10Wellcome Sanger Institute, Hinxton, Cambridgeshire CB10 1SA, UK; 11European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge CB10 1SD, UK; 12Integrative Marine Ecology Department, Stazione Zoologica Anton Dohrn, 80121 Naples, Italy

                Author information
                http://orcid.org/0000-0001-7410-6295
                http://orcid.org/0000-0003-4717-4721
                http://orcid.org/0000-0001-5031-8023
                http://orcid.org/0000-0002-6913-8417
                http://orcid.org/0000-0003-2094-3324
                http://orcid.org/0000-0002-1413-3424
                http://orcid.org/0000-0003-2355-5888
                http://orcid.org/0000-0002-0534-9565
                http://orcid.org/0000-0003-3889-8648
                http://orcid.org/0000-0002-1446-5832
                http://orcid.org/0000-0003-0632-9838
                http://orcid.org/0000-0003-3024-7568
                http://orcid.org/0000-0002-6762-6011
                http://orcid.org/0000-0002-3756-9036
                http://orcid.org/0000-0002-9739-6333
                http://orcid.org/0000-0002-7249-1668
                http://orcid.org/0000-0003-4623-8105
                http://orcid.org/0000-0001-5293-4778
                Article
                9509184
                10.1101/gr.278295.123
                11067885
                38621828
                5c0f8392-28fb-4adc-931a-07aa605bc94e
                © 2024 Plessy et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 July 2023
                : 28 February 2024
                Page count
                Pages: 15
                Funding
                Funded by: NFR-FRIBIO
                Award ID: 204891/F20
                Funded by: Norwegian Research Council , doi 10.13039/501100005416;
                Funded by: Japan Society for the Promotion of Science , doi 10.13039/501100001691;
                Funded by: Okinawa Institute of Science and Technology Graduate University , doi 10.13039/501100004199;
                Award ID: PID2019-110562GB-I00
                Award ID: PID2022-141627NB-I00
                Funded by: Spanish Ministerio de Ciencia e Innovación
                Funded by: ICREA Acadèmia
                Award ID: Ac2215698
                Award ID: 2021-SGR00372
                Funded by: Generalitat de Catalunya , doi 10.13039/501100002809;
                Award ID: 2017BP00139
                Funded by: Generalitat de Catalunya , doi 10.13039/501100002809;
                Award ID: 2019IRBio001
                Funded by: Universitat de Barcelona , doi 10.13039/501100005774;
                Award ID: FPU18/02414
                Funded by: Ministerio de Educación y cultura
                Award ID: PREDOC2020/58
                Funded by: Universitat de Barcelona , doi 10.13039/501100005774;
                Award ID: MS12
                Funded by: Ministerio de Universidades
                Categories
                Research

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