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      Deformed Wing Virus in Two Widespread Invasive Ants: Geographical Distribution, Prevalence, and Phylogeny

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          Abstract

          Spillover of honey bee viruses have posed a significant threat to pollination services, triggering substantial effort in determining the host range of the viruses as an attempt to understand the transmission dynamics. Previous studies have reported infection of honey bee viruses in ants, raising the concern of ants serving as a reservoir host. Most of these studies, however, are restricted to a single, local ant population. We assessed the status (geographical distribution/prevalence/viral replication) and phylogenetic relationships of honey bee viruses in ants across the Asia–Pacific region, using deformed wing virus (DWV) and two widespread invasive ants, Paratrechina longicornis and Anoplolepis gracilipes, as the study system. DWV was detected in both ant species, with differential geographical distribution patterns and prevenance levels between them. These metrics, however, are consistent across the geographical range of the same ant species. Active replication was only evident in P. longicornis. We also showed that ant-associated DWV is genetically similar to that isolated from Asian populations of honey bees, suggesting that local acquisition of DWV by the invasive ants may have been common at least in some of our sampled regions. Transmission efficiency of DWV to local arthropods mediated by ant, however, may vary across ant species.

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          Most cited references62

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

          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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            Interactive Tree Of Life (iTOL) v4: recent updates and new developments

            Abstract The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations.
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              RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference

              Abstract Motivation Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets. Results We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric. Availability and implementation The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng . RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/ . Supplementary information Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Viruses
                Viruses
                viruses
                Viruses
                MDPI
                1999-4915
                15 November 2020
                November 2020
                : 12
                : 11
                : 1309
                Affiliations
                [1 ]Research Institute for Sustainable Humanosphere, Kyoto University, Kyoto 611-0011, Japan; chunyitonylin@ 123456gmail.com (C.-Y.L.); lee.chihchi.54m@ 123456st.kyoto-u.ac.jp (C.-C.L.); max7297@ 123456gmail.com (H.-W.H.)
                [2 ]Laboratory of Insect Ecology, Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
                [3 ]Department of Entomology, National Chung Hsing University, Taichung 402204, Taiwan; ysnai@ 123456nchu.edu.tw
                [4 ]Department of Entomology, University of California, 900 University Avenue, Riverside, CA 92521, USA; chowyang.lee@ 123456ucr.edu
                [5 ]Department of Subtropical Agro-Environmental Sciences, University of the Ryukyus, Senbaru 1, Nishihara, Okinawa 903-0213, Japan; tsujik@ 123456agr.u-ryukyu.ac.jp
                [6 ]Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
                Author notes
                [* ]Correspondence: scottyistheman33@ 123456gmail.com ; Tel.: +886-4-2284-0361 (ext. 540)
                Author information
                https://orcid.org/0000-0001-7204-1360
                https://orcid.org/0000-0002-8778-1449
                https://orcid.org/0000-0003-0104-6839
                https://orcid.org/0000-0001-8279-4104
                https://orcid.org/0000-0003-0967-5170
                Article
                viruses-12-01309
                10.3390/v12111309
                7696054
                33203145
                1f3a01bd-d967-4e85-a2ee-4dede7082337
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 October 2020
                : 13 November 2020
                Categories
                Article

                Microbiology & Virology
                deformed wing virus,honey bee,longhorn crazy ant,virus spillover,yellow crazy ant

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