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      Genetic Drift and Purifying Selection Shaped Mitochondrial Genome Variation in the High Royal Jelly-Producing Honeybee Strain ( Apis mellifera ligustica)

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          Abstract

          Mitochondrial genomes (mitogenomes) are involved in cellular energy metabolism and have been shown to undergo adaptive evolution in organisms with increased energy-consuming activities. The genetically selected high royal jelly-producing bees (RJBs, Apis mellifera ligustica) in China can produce 10 times more royal jelly, a highly nutritional and functional food, relative to unselected Italian bees (ITBs). To test for potential adaptive evolution of RJB mitochondrial genes, we sequenced mitogenomes from 100 RJBs and 30 ITBs. Haplotype network and phylogenetic analysis indicate that RJBs and ITBs are not reciprocally monophyletic but mainly divided into the RJB- and ITB-dominant sublineages. The RJB-dominant sublineage proportion is 6-fold higher in RJBs (84/100) than in ITBs (4/30), which is mainly attributable to genetic drift rather than positive selection. The RJB-dominant sublineage exhibits a low genetic diversity due to purifying selection. Moreover, mitogenome abundance is not significantly different between RJBs and ITBs, thereby rejecting the association between mitogenome copy number and royal jelly-producing performance. Our findings demonstrate low genetic diversity levels of RJB mitogenomes and reveal genetic drift and purifying selection as potential forces driving RJB mitogenome evolution.

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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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            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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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                09 February 2022
                2022
                : 13
                : 835967
                Affiliations
                [ 1 ] Institute of Apicultural Research , Chinese Academy of Agricultural Sciences , Beijing, China
                [ 2 ] CREA Research Centre for Agriculture and Environment , Bologna, Italy
                Author notes

                Edited by: Sankar Subramanian, University of the Sunshine Coast, Australia

                Reviewed by: Michael Lattorff, International Centre of Insect Physiology and Ecology (ICIPE), Kenya

                Lianfei Cao, Zhejiang Academy of Agricultural Sciences, China

                *Correspondence: Jianke Li, apislijk@ 123456126.com

                This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics

                [ † ]

                These authors have contributed equally to this work

                Article
                835967
                10.3389/fgene.2022.835967
                8864236
                ef3a4a2d-dfb2-40ad-94eb-ff40f29a7367
                Copyright © 2022 Ma, Hu, Costa and Li.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 December 2021
                : 18 January 2022
                Funding
                Funded by: Beijing Municipal Natural Science Foundation , doi 10.13039/501100005089;
                Funded by: Agricultural Science and Technology Innovation Program , doi 10.13039/501100012421;
                Categories
                Genetics
                Original Research

                Genetics
                genetic drift,mtdna copy number,positive selection,purifying selection,royal jelly
                Genetics
                genetic drift, mtdna copy number, positive selection, purifying selection, royal jelly

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