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      Cross-species gene enrichment revealed a single population of Hilsa shad ( Tenualosa ilisha) with low genetic variation in Bangladesh waters

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          Abstract

          Tenualosa ilisha is a popular anadromous and significant trans-boundary fish. For sustainable management and conservation of this fish, drawing an appropriate picture reflecting population status of this species is very essential based on their all-strategic habitats in Bangladesh. In this study, 139 samples from 18 sites were collected and cross-species gene enrichment method was applied. Like most of the Clupeiforms, nucleotide diversity of this shad was very low (0.001245–0.006612). Population differences between most of the locations were low and not significant ( P > 0.05). However, P values of a few locations were significant ( P < 0.05) but their pairwise F ST values were very poor (0.0042–0.0993), which is inadequate to recognize any local populations. Our study revealed that the presence of a single population in the Bangladesh waters with some admixtured individuals, which may contain partial genes from other populations. Most of the individuals were admixed without showing any precise grouping in the ML IQtree and Network, which might due to their highly migratory nature. Fishes from haors and small coastal rivers were not unique and no genetic differences between migratory cohorts. The hilsa shad fishery should be managed considering it as a single panmictic population in Bangladesh with low genetic diversity.

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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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            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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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Contributors
                chli@shou.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                2 June 2021
                2 June 2021
                2021
                : 11
                : 11560
                Affiliations
                [1 ]GRID grid.412514.7, ISNI 0000 0000 9833 2433, Shanghai Universities Key Laboratory of Marine Animal Taxonomy and Evolution, , Shanghai Ocean University, ; Shanghai, 201306 China
                [2 ]GRID grid.443016.4, ISNI 0000 0004 4684 0582, Department of Zoology, , Jagannath University, ; Dhaka, 1100 Bangladesh
                Author information
                http://orcid.org/0000-0003-3075-1756
                Article
                90864
                10.1038/s41598-021-90864-6
                8173019
                34078978
                c1530572-eae5-4756-8645-a2dc1825c71a
                © The Author(s) 2021

                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
                : 8 December 2020
                : 18 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003399, Science and Technology Commission of Shanghai Municipality;
                Award ID: 19410740500
                Award ID: 19410740500
                Award ID: 19410740500
                Award ID: 19410740500
                Award ID: 19410740500
                Award Recipient :
                Funded by: Shanghai Collaborative Innovation for Aquatic Animal Genetics and Breeding Project
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                ecology,ecological genetics
                Uncategorized
                ecology, ecological genetics

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