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      Prevalent bee venom genes evolved before the aculeate stinger and eusociality

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          Abstract

          Background

          Venoms, which have evolved numerous times in animals, are ideal models of convergent trait evolution. However, detailed genomic studies of toxin-encoding genes exist for only a few animal groups. The hyper-diverse hymenopteran insects are the most speciose venomous clade, but investigation of the origin of their venom genes has been largely neglected.

          Results

          Utilizing a combination of genomic and proteo-transcriptomic data, we investigated the origin of 11 toxin genes in 29 published and 3 new hymenopteran genomes and compiled an up-to-date list of prevalent bee venom proteins. Observed patterns indicate that bee venom genes predominantly originate through single gene co-option with gene duplication contributing to subsequent diversification.

          Conclusions

          Most Hymenoptera venom genes are shared by all members of the clade and only melittin and the new venom protein family anthophilin1 appear unique to the bee lineage. Most venom proteins thus predate the mega-radiation of hymenopterans and the evolution of the aculeate stinger.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12915-023-01656-5.

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          Most cited references63

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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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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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
                atjcoludar@gmail.com
                marianavelasque@gmail.com
                senoner@rostlab.org
                Thomas.Timm@biochemie.med.uni-giessen.de
                carola.greve@senckenberg.de
                tbg-laborzentrum@senckenberg.de
                deepak.gupta@senckenberg.de
                Guenter.Lochnit@biochemie.med.uni-giessen.de
                mheinzinger@rostlab.org
                andreas.vilcinskas@ime.fraunhofer.de
                ros.gloag@sydney.edu.au
                bharpur@purdue.edu
                lars@cgae.de
                assistant@rostlab.org
                timothy.jackson@unimelb.edu.au
                sebastien.dutertre@umontpellier.frl
                E.Stolle@leibniz-lib.de
                bmvr@reumont.net
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                23 October 2023
                23 October 2023
                2023
                : 21
                : 229
                Affiliations
                [1 ]Justus Liebig University of Gießen, Institute for Insect Biotechnology, ( https://ror.org/033eqas34) Heinrich-Buff-Ring 58, 35392 Giessen, Germany
                [2 ]Department of Informatics, Bioinformatics and Computational Biology, i12, Technical University of Munich, ( https://ror.org/02kkvpp62) Boltzmannstr. 3, Garching, 85748 Munich, Germany
                [3 ]Genomics & Regulatory Systems Unit, Okinawa Institute of Science & Technology, ( https://ror.org/02qg15b79) Tancha, Okinawa 1919 Japan
                [4 ]Protein Analytics, Institute of Biochemistry, Justus Liebig University, ( https://ror.org/033eqas34) Friedrichstrasse 24, 35392 Giessen, Germany
                [5 ]LOEWE Centre for Translational Biodiversity Genomics (TBG), ( https://ror.org/0396gab88) Senckenberganlage 25, 60325 Frankfurt, Germany
                [6 ]Fraunhofer Institute for Molecular Biology and Applied Ecology, Department of Bioresources, ( https://ror.org/03j85fc72) Ohlebergsweg 12, 35392 Giessen, Germany
                [7 ]Rosalyn Gloag - School of Life and Environmental Sciences, The University of Sydney, ( https://ror.org/0384j8v12) Sydney, NSW 2006 Australia
                [8 ]Brock A. Harpur – Department of Entomology, Purdue University, ( https://ror.org/02dqehb95) 901 W. State Street, West Lafayette, IN 47907 USA
                [9 ]GRID grid.452935.c, ISNI 0000 0001 2216 5875, Leibniz Institute for the Analysis of Biodiversity Change, Zoological Research Museum Alexander Koenig, Centre of Molecular Biodiversity Research, ; Adenauerallee 160, 53113 Bonn, Germany
                [10 ]Australian Venom Research Unit, Department of Biochemistry and Pharmacology, University of Melbourne, ( https://ror.org/01ej9dk98) Grattan Street, Parkville, Viktoria 3010 Australia
                [11 ]GRID grid.462008.8, IBMM, Université Montpellier, CNRS, ENSCM, ; 34095 Montpellier, France
                [12 ]Faculty of Biological Sciences, Group of Applied Bioinformatics, Goethe University Frankfurt, ( https://ror.org/04cvxnb49) Max-Von-Laue Str. 13, 60438 Frankfurt, Germany
                Author information
                http://orcid.org/0000-0002-9073-4758
                Article
                1656
                10.1186/s12915-023-01656-5
                10591384
                37867198
                017eddb7-661e-42c1-abbe-bbac40f1c84f
                © BioMed Central Ltd., part of Springer Nature 2023

                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/. 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
                : 27 January 2023
                : 29 June 2023
                Funding
                Funded by: DFG
                Award ID: RE3454/6-1
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Life sciences
                hymenoptera venom,bee toxins,solitary bee venom,proteo-transcriptomics,genomics,venom gene evolution,machine learning,melittin,apamin,aculeatoxins

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