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      The newly assembled chloroplast genome of Aeluropus littoralis: molecular feature characterization and phylogenetic analysis with related species

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          Abstract

          Aeluropus littoralis, a halophyte grass, is widely distributed from the Mediterranean to the Indian subcontinent through the Mongolian Gobi. This model halophyte has garnered increasing attention owing to its use as forage and its high tolerance to environmental stressors. The chloroplast genomes of many plants have been extensively examined for molecular, phylogenetic and transplastomic applications. However, no published research on the A. littoralis chloroplast (cp) genome was discovered. Here, the entire chloroplast genome of A. littoralis was assembled implementing accurate long-read sequences. The entire chloroplast genome, with an estimated length of 135,532 bp (GC content: 38.2%), has a quadripartite architecture and includes a pair of inverted repeat (IR) regions, IRa and IRb (21,012 bp each), separated by a large and a small single-copy regions (80,823 and 12,685 bp, respectively). The features of A. littoralis consist of 133 genes that synthesize 87 peptides, 38 transfer RNAs, and 8 ribosomal RNAs. Of these genes, 86 were unique, whereas 19 were duplicated in IR regions. Additionally, a total of forty-six simple sequence repeats, categorized into 32-mono, four-di, two-tri, and eight-tetranucleotides, were discovered. Furthermore, ten sets of repeats greater than 20 bp were located primarily in the LSC region. Evolutionary analysis based on chloroplast sequence data revealed that A. littoralis with A. lagopoides and A. sinensis belong to the Aeluropodinae subtribe, which is a sister to the Eleusininae in the tribe Cynodonteae and the subfamily Chloridoideae. This subfamily belongs to the PACMAD clade, which contains the majority of the C4 photosynthetic plants in the Poaceae. The newly constructed A. littoralis cp genome offers valuable knowledge for DNA barcoding, phylogenetic, transplastomic research, and other biological studies.

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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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              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
                wromdhane@ksu.edu.sa
                ahassairi@ksu.edu.sa
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 March 2024
                18 March 2024
                2024
                : 14
                : 6472
                Affiliations
                College of Food and Agricultural Sciences, Plant Production Department, King Saud University, ( https://ror.org/02f81g417) P.O. Box 2460, 11451 Riyadh, Saudi Arabia
                Article
                57141
                10.1038/s41598-024-57141-8
                10948853
                38499663
                3ec97dd7-0877-4afa-8f4e-233b7556f578
                © 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/.

                History
                : 4 January 2024
                : 14 March 2024
                Funding
                Funded by: National Plan for Science, Technology and Innovation (MAARIFAH), King Abdul Aziz City for Science and Technology, Kingdom of Saudi Arabia
                Award ID: 2-17-04-001-0046
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                comparative genomics,dna sequencing,next-generation sequencing,phylogenomics,genetic markers

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