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      Monophyletic blowflies revealed by phylogenomics

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          Abstract

          Background

          Blowflies are ubiquitous insects, often shiny and metallic, and the larvae of many species provide important ecosystem services (e.g., recycling carrion) and are used in forensics and debridement therapy. Yet, the taxon has repeatedly been recovered to be para- or polyphyletic, and the lack of a well-corroborated phylogeny has prevented a robust classification.

          Results

          We here resolve the relationships between the different blowfly subclades by including all recognized subfamilies in a phylogenomic analysis using 2221 single-copy nuclear protein-coding genes of Diptera. Maximum likelihood (ML), maximum parsimony (MP), and coalescent-based phylogeny reconstructions all support the same relationships for the full data set. Based on this backbone phylogeny, blowflies are redefined as the most inclusive monophylum within the superfamily Oestroidea not containing Mesembrinellidae, Mystacinobiidae, Oestridae, Polleniidae, Sarcophagidae, Tachinidae, and Ulurumyiidae. The constituent subfamilies are re-classified as Ameniinae (including the Helicoboscinae, syn. nov.), Bengaliinae, Calliphorinae (including Aphyssurinae, syn. nov., Melanomyinae, syn. nov., and Toxotarsinae, syn. nov.), Chrysomyinae, Luciliinae, Phumosiinae, Rhiniinae stat. rev., and Rhinophorinae stat. rev. Metallic coloration in the adult is shown to be widespread but does not emerge as the most likely ground plan feature.

          Conclusions

          Our study provides the first phylogeny of oestroid calyptrates including all blowfly subfamilies. This allows settling a long-lasting controversy in Diptera by redefining blowflies as a well-supported monophylum, and blowfly classification is adjusted accordingly. The archetypical blowfly trait of carrion-feeding maggots most likely evolved twice, and the metallic color may not belong to the blowfly ground plan.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12915-021-01156-4.

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          Most cited references76

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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              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

                Contributors
                zhangdong_bjfu@bjfu.edu.cn
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                27 October 2021
                27 October 2021
                2021
                : 19
                : 230
                Affiliations
                [1 ]GRID grid.66741.32, ISNI 0000 0001 1456 856X, School of Ecology and Nature Conservation, , Beijing Forestry University, ; Beijing, China
                [2 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Natural History Museum of Denmark, , University of Copenhagen, ; Copenhagen, Denmark
                [3 ]GRID grid.5963.9, Evolutionary Biology & Ecology, , University of Freiburg, ; Freiburg, Germany
                [4 ]GRID grid.452935.c, ISNI 0000 0001 2216 5875, Zoologisches Forschungsmuseum Alexander Koenig (ZFMK)/Zentrum für Molekulare Biodiversitätsforschung (ZMB), ; Bonn, Germany
                [5 ]GRID grid.1016.6, ISNI 0000 0001 2173 2719, Australian National Insect Collection, , CSIRO National Research Collections Australia (NRCA), ; Canberra, Australia
                [6 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Department of Biological Sciences, , National University of Singapore, ; Singapore, Singapore
                [7 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Tropical Marine Science Institute, , National University of Singapore, ; Singapore, Singapore
                [8 ]GRID grid.422371.1, ISNI 0000 0001 2293 9957, Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Center for Integrative Biodiversity Discovery, ; Berlin, Germany
                [9 ]GRID grid.242287.9, ISNI 0000 0004 0461 6769, Department of Entomology, , California Academy of Sciences, ; San Francisco, USA
                Article
                1156
                10.1186/s12915-021-01156-4
                8555136
                34706743
                3f4ae66c-3c59-4923-bfbb-cb2093e0d435
                © The Author(s) 2021

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 7 April 2020
                : 23 September 2021
                Funding
                Funded by: Beijing Forestry University Outstanding Young Talent Cultivation Project
                Award ID: 2019JQ03018
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31872964
                Award ID: 31572305
                Award Recipient :
                Funded by: Postdoctoral Innovative Talents Support Program
                Award ID: BX20190042
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Life sciences
                calyptratae,transcriptome,genome,phylogeny,coloration
                Life sciences
                calyptratae, transcriptome, genome, phylogeny, coloration

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