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      Western Bluetongue virus serotype 3 in Sardinia, diagnosis and characterization

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          Over the last 20 years, Italy has experienced multiple incursions of different serotypes of Bluetongue virus ( BTV), a Culicoides‐borne arbovirus, the causative agent of bluetongue ( BT), a major disease of ruminants. The majority of these incursions originated from Northern Africa, likely because of wind‐blown dissemination of infected midges. Here, we report the first identification of BTV‐3 in Sardinia, Italy. BTV‐3 circulation was evidenced in sentinel animals located in the province of Sud Sardegna on September 19, 2018. Prototype strain BTV‐3 SAR2018 was isolated on cell culture. BTV‐3 SAR2018 sequence and partial sequences obtained by next‐generation sequencing from nucleic acids purified from the isolate and blood samples, respectively, were demonstrated to be almost identical (99–100% of nucleotide identity) to BTV‐3 TUN2016 identified in Tunisia in 2016 and 2017, a scenario already observed in past incursions of other BTV serotypes originating from Northern Africa.

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          Most cited references 25

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          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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            A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

            The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page:
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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from

                Author and article information

                Transbound Emerg Dis
                Transbound Emerg Dis
                Transboundary and Emerging Diseases
                John Wiley and Sons Inc. (Hoboken )
                19 March 2019
                May 2019
                : 66
                : 3 ( doiID: 10.1111/tbed.2019.66.issue-3 )
                : 1426-1431
                [ 1 ] Istituto Zooprofilattico Sperimentale della Sardegna Cagliari Italy
                [ 2 ] OIE Reference Laboratory for Bluetongue Teramo Italy
                [ 3 ] Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise (IZSAM) National Reference Center for Whole Genome Sequencing of microbial Pathogens: Database and Bioinformatic Analysis Teramo Italy
                [ 4 ] Laboratoire de virologie Institut de la Recherche Vétérinaire de Tunisie (IRVT) Univérsité de Tunis El Manar Tunis Tunisia
                Author notes
                [* ] Correspondence

                Alessio Lorusso, Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise (IZSAM), Teramo, Italy.

                Email: a.lorusso@

                © 2019 The Authors. Transboundary and Emerging Diseases published by Blackwell Verlag GmbH.

                This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 3, Tables: 0, Pages: 6, Words: 3823
                Funded by: Horizon 2020 , open-funder-registry 10.13039/100010661;
                Short Communication
                Short Communications
                Custom metadata
                May 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.1 mode:remove_FC converted:12.11.2019


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