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      Insights from Melipona bicolor hybrid genome assembly: a stingless bee genome with chromosome-level scaffold

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          Abstract

          Background

          The highly eusocial stingless bees are crucial pollinators of native and agricultural ecosystems. Nevertheless, genomic studies within this bee tribe remain scarce. We present the genome assembly of the stingless bee Melipona bicolo r. This bee is a remarkable exception to the typical single-queen colony structure, since in this species, multiple queens may coexist and share reproductive duties, resulting in genetically diverse colonies with weak kinship connections. As the only known genuinely polygynous bee, M. bicolor’s genome provides a valuable resource for investigating sociality beyond kin selection.

          Results

          The genome was assembled employing a hybrid approach combining short and long reads, resulting in 241 contigs spanning 259 Mb (N50 of 6.2 Mb and 97.5% complete BUSCOs). Comparative analyses shed light on some evolutionary aspects of stingless bee genomics, including multiple chromosomal rearrangements in Melipona. Additionally, we explored the evolution of venom genes in M. bicolor and other stingless bees, revealing that, apart from two genes, the conserved repertoire of venom components remains under purifying selection in this clade.

          Conclusion

          This study advances our understanding of stingless bee genomics, contributing to the conservation efforts of these vital pollinators and offering insights into the evolutionary mechanisms driving their unique adaptations.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-024-10075-x.

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          Most cited references57

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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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            BLAST+: architecture and applications

            Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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              Search and clustering orders of magnitude faster than BLAST.

              Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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                Author and article information

                Contributors
                natalia.de.souza.araujo@ulb.be
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                13 February 2024
                13 February 2024
                2024
                : 25
                : 171
                Affiliations
                [1 ]Evolutionary Biology & Ecology, Université Libre de Bruxelles – ULB, ( https://ror.org/01r9htc13) Brussels, Belgium
                [2 ]Laboratory of Genetics and Evolution of Bees, Bioscience Institute, Universidade de São Paulo – USP, ( https://ror.org/036rp1748) São Paulo, Brazil
                [3 ]Gene Expression and Evolution Laboratory, Bioscience Institute, Universidade de São Paulo – USP, ( https://ror.org/036rp1748) São Paulo, Brazil
                Author information
                http://orcid.org/0000-0002-0074-6844
                http://orcid.org/0000-0003-2495-889X
                http://orcid.org/0000-0002-0070-6404
                http://orcid.org/0000-0003-1477-101X
                Article
                10075
                10.1186/s12864-024-10075-x
                10863234
                38350872
                f4a1d63d-c6e6-4898-9047-ed4c126b6ede
                © The Author(s) 2024

                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
                : 9 October 2023
                : 1 February 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: 2019/23186-9; 2016/24669-5
                Award ID: 2019/23186-9; 2016/24669-5
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002661, Fonds De La Recherche Scientifique - FNRS;
                Award ID: 40005980
                Award Recipient :
                Funded by: Fondation Universitaire de Belgique
                Funded by: FundRef http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 307356/2019-1
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Genetics
                meliponini,genome assembly,venom genes,eusocial bees,polygyny
                Genetics
                meliponini, genome assembly, venom genes, eusocial bees, polygyny

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