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      Phylogenomic Discordance in the Eared Seals is best explained by Incomplete Lineage Sorting following Explosive Radiation in the Southern Hemisphere

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          Abstract

          The phylogeny and systematics of fur seals and sea lions (Otariidae) have long been studied with diverse data types, including an increasing amount of molecular data. However, only a few phylogenetic relationships have reached acceptance because of strong gene-tree species tree discordance. Divergence times estimates in the group also vary largely between studies. These uncertainties impeded the understanding of the biogeographical history of the group, such as when and how trans-equatorial dispersal and subsequent speciation events occurred. Here, we used high-coverage genome-wide sequencing for 14 of the 15 species of Otariidae to elucidate the phylogeny of the family and its bearing on the taxonomy and biogeographical history. Despite extreme topological discordance among gene trees, we found a fully supported species tree that agrees with the few well-accepted relationships and establishes monophyly of the genus Arctocephalus. Our data support a relatively recent trans-hemispheric dispersal at the base of a southern clade, which rapidly diversified into six major lineages between 3 and 2.5 Ma. Otaria diverged first, followed by Phocarctos and then four major lineages within Arctocephalus. However, we found Zalophus to be nonmonophyletic, with California (Zalophus californianus) and Steller sea lions (Eumetopias jubatus) grouping closer than the Galapagos sea lion (Zalophus wollebaeki) with evidence for introgression between the two genera. Overall, the high degree of genealogical discordance was best explained by incomplete lineage sorting resulting from quasi-simultaneous speciation within the southern clade with introgresssion playing a subordinate role in explaining the incongruence among and within prior phylogenetic studies of the family. [Hybridization; ILS; phylogenomics; Pleistocene; Pliocene; monophyly.]

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Journal
                Systematic Biology
                Oxford University Press (OUP)
                1063-5157
                1076-836X
                July 01 2021
                June 16 2021
                December 26 2020
                July 01 2021
                June 16 2021
                December 26 2020
                : 70
                : 4
                : 786-802
                Affiliations
                [1 ]Escola de Ciências da Saúde e da Vida, Pontifícia Universidade Católica do Rio Grande do Sul, 90619-900 Porto Alegre, RS, Brazil
                [2 ]Laboratório de Ecologia de Mamíferos, Universidade do Vale do Rio dos Sinos, São Leopoldo, RS, Brazil
                [3 ]GEMARS, Grupo de Estudos de Mamíferos Aquáticos do Rio Grande do Sul, 95560-000 Torres, RS, Brazil
                [4 ]Centro Nacional Patagónico - CENPAT, CONICET, Puerto Madryn, Argentina
                [5 ]Centro para la Sostenibilidad Ambiental, Universidad Peruana Cayetano Heredia, Lima, Peru
                [6 ]Centro de Investigación y Gestión de Recursos Naturales (CIGREN), Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
                [7 ]Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, NOAA, La Jolla, USA
                [8 ]Departamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
                [9 ]Colegio de Ciencias Biológicas y Ambientales, COCIBA, Universidad San Francisco de Quito, Quito, Ecuador
                [10 ]Department of Biology, San Francisco State University, 1800 Holloway Ave, San Francisco, CA, USA
                [11 ]Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand
                [12 ]Department of Zoology, University of Otago, Dunedin, New Zealand
                [13 ]Unit for Basic and Applied Microbiology, School of Natural Sciences, Universidad Autónoma de Querétaro, Querétaro, Mexico
                [14 ]Instituto Politecnico Nacional, Centro Interdisciplinario de Ciencias Marinas, La Paz, Mexico
                [15 ]Department of Environmental Affairs, Oceans and Coasts, Cape Town, South Africa
                [16 ]Department Biologie II, Division of Evolutionary Biology, Ludwig-Maximilians-Universität München, Münich, Germany
                Article
                10.1093/sysbio/syaa099
                33367817
                0e8b1a0f-7491-4d83-a908-c9bdc19dcf04
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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