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      Whole mitogenomes reveal that NW Africa has acted both as a source and a destination for multiple human movements

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          Abstract

          Despite being enclosed between the Mediterranean Sea and the Sahara Desert, North Africa has been the scenario of multiple human migrations that have shaped the genetic structure of its present-day populations. Despite its richness, North Africa remains underrepresented in genomic studies. To overcome this, we have sequenced and analyzed 264 mitogenomes from the Algerian Chaoui-speaking Imazighen (a.k.a. Berbers) living in the Aurès region. The maternal genetic composition of the Aurès is similar to Arab populations in the region, dominated by West Eurasian lineages with a moderate presence of M1/U6 North African and L sub-Saharan lineages. When focusing on the time and geographic origin of the North African specific clades within the non-autochthonous haplogroups, different geographical neighboring regions contributed to the North African maternal gene pool during time periods that could be attributed to previously suggested admixture events in the region, since Paleolithic times to recent historical movements such as the Arabization. We have also observed the role of North Africa as a source of geneflow mainly in Southern European regions since Neolithic times. Finally, the present work constitutes an effort to increase the representation of North African populations in genetic databases, which is key to understand their history.

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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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            Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

            Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.
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              jModelTest 2: more models, new heuristics and parallel computing.

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                Author and article information

                Contributors
                david.comas@upf.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 June 2023
                27 June 2023
                2023
                : 13
                : 10395
                Affiliations
                [1 ]GRID grid.5612.0, ISNI 0000 0001 2172 2676, Departament de Medicina i Ciències de la Vida, Institut de Biologia Evolutiva (CSIC-UPF), , Universitat Pompeu Fabra, ; Barcelona, Spain
                [2 ]GRID grid.420190.e, ISNI 0000 0001 2293 1293, Laboratorie de Biologie Cellulaire et Moléculaire, Faculté des Sciences Biologiques, , Université des Sciences et de la Technologie Houari Boumediene, ; Alger, Algeria
                Article
                37549
                10.1038/s41598-023-37549-4
                10300034
                37369751
                dc6b5aa3-0c91-431c-a6d9-197c2439ec4f
                © The Author(s) 2023

                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
                : 26 April 2023
                : 23 June 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100011033, Agencia Estatal de Investigación;
                Award ID: CEX2018-000792-M
                Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;
                Award ID: PID2019-106485GB-I00
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                biological anthropology,genetic variation
                Uncategorized
                biological anthropology, genetic variation

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