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      Complete mitochondrial genomes of three reef forming Acropora corals (Acroporidae, Scleractinia) from Chagos Archipelago, Indian Ocean

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      Biodiversity Data Journal

      Pensoft Publishers

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          Abstract

          We present the first mitochondrial genomes from Chagos Archipelago, Indian Ocean, of three putative species of reef forming Acropora (Acropora aff. tenuis, Acropora aff. cytherea and Acropora aff. orbicularis). The circular genome consists respectively of 18,334 bp, 18,353 bp and 18,584 bp. All mitochondrial genomes recovered comprise 13 protein-coding genes, two transfer RNA genes and two ribosomal RNA genes, with an overall GC content ranging from 37.9% to 38.0%. These new genomic data contribute to our increased understanding of genus Acropora and its species boundaries, ultimately aiding species monitoring and conservation efforts.

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          Most cited references 42

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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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            ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

            Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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              Canu: scalable and accurate long-read assembly via adaptive k -mer weighting and repeat separation

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                Author and article information

                Contributors
                Journal
                Biodiversity Data Journal
                BDJ
                Pensoft Publishers
                1314-2828
                1314-2836
                September 30 2021
                September 30 2021
                : 9
                Article
                10.3897/BDJ.9.e72762
                © 2021

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