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      The genomic origins of the Bronze Age Tarim Basin mummies

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          Abstract

          The identity of the earliest inhabitants of Xinjiang, in the heart of Inner Asia, and the languages that they spoke have long been debated and remain contentious 1 . Here we present genomic data from 5 individuals dating to around 3000–2800  bc from the Dzungarian Basin and 13 individuals dating to around 2100–1700  bc from the Tarim Basin, representing the earliest yet discovered human remains from North and South Xinjiang, respectively. We find that the Early Bronze Age Dzungarian individuals exhibit a predominantly Afanasievo ancestry with an additional local contribution, and the Early–Middle Bronze Age Tarim individuals contain only a local ancestry. The Tarim individuals from the site of Xiaohe further exhibit strong evidence of milk proteins in their dental calculus, indicating a reliance on dairy pastoralism at the site since its founding. Our results do not support previous hypotheses for the origin of the Tarim mummies, who were argued to be Proto-Tocharian-speaking pastoralists descended from the Afanasievo 1, 2 or to have originated among the Bactria–Margiana Archaeological Complex 3 or Inner Asian Mountain Corridor cultures 4 . Instead, although Tocharian may have been plausibly introduced to the Dzungarian Basin by Afanasievo migrants during the Early Bronze Age, we find that the earliest Tarim Basin cultures appear to have arisen from a genetically isolated local population that adopted neighbouring pastoralist and agriculturalist practices, which allowed them to settle and thrive along the shifting riverine oases of the Taklamakan Desert.

          Abstract

          A genomic analysis of human remains from the Bronze Age provides insights into the origin of the Tarim Basin mummies from the Xinjiang region.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

            Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data. Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl. Contact: peter@biomatters.com
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              Second-generation PLINK: rising to the challenge of larger and richer datasets

              PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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                Author and article information

                Contributors
                ning@shh.mpg.de
                krause@shh.mpg.de
                warinner@shh.mpg.de
                cwjeong@snu.ac.kr
                cuiyq@jlu.edu.cn
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                27 October 2021
                27 October 2021
                2021
                : 599
                : 7884
                : 256-261
                Affiliations
                [1 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, School of Life Sciences, , Jilin University, ; Changchun, China
                [2 ]GRID grid.469873.7, ISNI 0000 0004 4914 1197, Max Planck Institute for the Science of Human History, ; Jena, Germany
                [3 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, , Chinese Academy of Sciences, ; Beijing, China
                [4 ]Xinjiang Institute of Cultural Relics and Archaeology, Ürümqi, China
                [5 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, School of Archaeology, , Jilin University, ; Changchun, China
                [6 ]GRID grid.32566.34, ISNI 0000 0000 8571 0482, MOE Key Laboratory of Western China’s Environmental Systems, College of Earth & Environmental Sciences, , Lanzhou University, ; Lanzhou, China
                [7 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, , Chinese Academy of Sciences, ; Beijing, China
                [8 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, College of Pharmacia Sciences, , Jilin University, ; Changchun, China
                [9 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Institute of Archaeological Science, , Fudan University, ; Shanghai, China
                [10 ]GRID grid.419518.0, ISNI 0000 0001 2159 1813, Max Planck Institute for Evolutionary Anthropology, ; Leipzig, Germany
                [11 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Anthropology, , Harvard University, ; Cambridge, MA USA
                [12 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, School of Biological Sciences, , Seoul National University, ; Seoul, Republic of Korea
                [13 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, Key Laboratory for Evolution of Past Life and Environment in Northeast Asia, Ministry of Education, , Jilin University, ; Changchun, China
                [14 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, Research Center for Chinese Frontier Archaeology of Jilin University, , Jilin University, ; Changchun, China
                Author information
                http://orcid.org/0000-0001-9259-2223
                http://orcid.org/0000-0002-8848-3961
                http://orcid.org/0000-0001-8892-1557
                http://orcid.org/0000-0002-7141-0002
                http://orcid.org/0000-0001-8005-759X
                http://orcid.org/0000-0003-3446-9125
                http://orcid.org/0000-0002-2860-0230
                http://orcid.org/0000-0001-9144-3920
                http://orcid.org/0000-0002-4528-5877
                http://orcid.org/0000-0003-3049-2352
                http://orcid.org/0000-0003-3702-5773
                Article
                4052
                10.1038/s41586-021-04052-7
                8580821
                34707286
                21290702-399b-4109-9264-fab6f064231e
                © 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 November 2020
                : 23 September 2021
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                © The Author(s), under exclusive licence to Springer Nature Limited 2021

                Uncategorized
                archaeology,evolutionary genetics,population genetics
                Uncategorized
                archaeology, evolutionary genetics, population genetics

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