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      Genomic history of human monkey pox infections in the Central African Republic between 2001 and 2018

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          Abstract

          Monkeypox is an emerging infectious disease, which has a clinical presentation similar to smallpox. In the two past decades, Central Africa has seen an increase in the frequency of cases, with many monkeypox virus (MPXV) isolates detected in the Democratic Republic of Congo (DRC) and the Central African Republic (CAR). To date, no complete MPXV viral genome has been published from the human cases identified in the CAR. The objective of this study was to sequence the full genome of 10 MPXV isolates collected during the CAR epidemics between 2001 and 2018 in order to determine their phylogenetic relationships among MPXV lineages previously described in Central Africa and West Africa. Our phylogenetic results indicate that the 10 CAR isolates belong to three lineages closely related to those found in DRC. The phylogenetic pattern shows that all of them emerged in the rainforest block of the Congo Basin. Since most human index cases in CAR occurred at the northern edge of western and eastern rainforests, transmissions from wild animals living in the rainforest is the most probable hypothesis. In addition, molecular dating estimates suggest that periods of intense political instability resulting in population movements within the country often associated also with increased poverty may have led to more frequent contact with host wild animals. The CAR socio-economic situation, armed conflicts and ecological disturbances will likely incite populations to interact more and more with wild animals and thus increase the risk of zoonotic spillover.

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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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            jModelTest 2: more models, new heuristics and parallel computing.

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              Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10

              Abstract The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.
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                Author and article information

                Contributors
                nicolas.berthet@pasteur.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 June 2021
                22 June 2021
                2021
                : 11
                : 13085
                Affiliations
                [1 ]GRID grid.429007.8, ISNI 0000 0004 0627 2381, The Center for Microbes, Development and Health, CAS Key Laboratory of Molecular Virology and Immunology, , Institut Pasteur of Shanghai-Chinese Academy of Sciences, Discovery and Molecular Characterization of Pathogens, ; No. 320 Yueyang Road, XuHui District, Shanghai, 200031 China
                [2 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, Institut Pasteur, Unité Environnement et Risque Infectieux, Cellule d’Intervention Biologique d’Urgence, ; Paris, France
                [3 ]GRID grid.418115.8, ISNI 0000 0004 1808 058X, Centre International de Recherches Médicales de Franceville (CIRMF), ; Franceville, Gabon
                [4 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, Institut Pasteur, Centre of Bioinformatics, Biostatistics and Integrative Biology (C3BI), ; Paris, France
                [5 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, Institut Pasteur, Emerging Diseases Epidemiology Unit, ; Paris, France
                [6 ]GRID grid.462844.8, ISNI 0000 0001 2308 1657, Institut de Systématique, Évolution, Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, UA, ; Paris, France
                [7 ]GRID grid.418512.b, Institut Pasteur de Bangui, ; Bangui, Central African Republic
                [8 ]GRID grid.36823.3c, ISNI 0000 0001 2185 090X, Unité Pasteur-CNAM Risques Infectieux et Emergents (PACRI), Conservatoire National des Arts et Métiers, ; Paris, France
                [9 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, Institut Pasteur, Unité d’Epidémiologie et Physiopathologie des Virus Oncogènes, Département de Virologie, ; Paris, France
                [10 ]GRID grid.4444.0, ISNI 0000 0001 2112 9282, Centre National de Recherche Scientifique (CNRS) UMR3569, ; Paris, France
                Article
                92315
                10.1038/s41598-021-92315-8
                8219716
                34158533
                47ac23cd-af03-474a-9b5a-f894ef80ebb3
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 March 2021
                : 2 June 2021
                Funding
                Funded by: External Cooperation Program of Chinese Academy of Sciences
                Award ID: 153211KYSB20160001
                Award Recipient :
                Funded by: a Shanghai Municipal Science and Technology Major Project
                Award ID: 2019SHZDZX02
                Award Recipient :
                Funded by: Chinese Academy of Sciences
                Funded by: Agence National de la Recherche-AFRIPOX
                Funded by: Fondation SCOR
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                phylogenetics,pox virus,viral evolution
                Uncategorized
                phylogenetics, pox virus, viral evolution

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