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A bioinformatics approach to identifying Wolbachia infections in arthropods

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Wolbachia, Insects, Bioinformatics, NCBI SRA, Anopheles

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      Abstract

      Wolbachia is the most widespread endosymbiont, infecting >20% of arthropod species, and capable of drastically manipulating the host’s reproductive mechanisms. Conventionally, diagnosis has relied on PCR amplification; however, PCR is not always a reliable diagnostic technique due to primer specificity, strain diversity, degree of infection and/or tissue sampled. Here, we look for evidence of Wolbachia infection across a wide array of arthropod species using a bioinformatic approach to detect the Wolbachia genes ftsZ, wsp, and the groE operon in next-generation sequencing samples available through the NCBI Sequence Read Archive. For samples showing signs of infection, we attempted to assemble entire Wolbachia genomes, and in order to better understand the relationships between hosts and symbionts, phylogenies were constructed using the assembled gene sequences. Out of the 34 species with positively identified infections, eight species of arthropod had not previously been recorded to harbor Wolbachia infection. All putative infections cluster with known representative strains belonging to supergroup A or B, which are known to only infect arthropods. This study presents an efficient bioinformatic approach for post-sequencing diagnosis and analysis of Wolbachia infection in arthropods.

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        MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

        We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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            Author and article information

            Affiliations
            [1 ]Department of Biological Sciences, State University of New York at Oswego , Oswego, NY, United States of America
            [2 ]Department of Biology, Syracuse University , Syracuse, NY, United States of America
            Contributors
            Journal
            PeerJ
            PeerJ
            peerj
            peerj
            PeerJ
            PeerJ Inc. (San Francisco, USA )
            2167-8359
            3 September 2018
            2018
            : 6
            6126470
            5486
            10.7717/peerj.5486
            (Editor)
            ©2018 Pascar and Chandler

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

            Funding
            Funded by: National Science Foundation
            Award ID: NSF DEB-1453298
            Funded by: Indiana University
            Award ID: ABI-1458641
            This work was supported by the National Science Foundation (NSF DEB-1453298 to Christopher Chandler, and ABI-1458641 to Indiana University). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Bioinformatics
            Entomology
            Evolutionary Studies
            Genomics
            Microbiology

            anopheles, ncbi sra, bioinformatics, insects, wolbachia

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