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      Major Population Expansion of East Asians Began before Neolithic Time: Evidence of mtDNA Genomes

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          Abstract

          It is a major question in archaeology and anthropology whether human populations started to grow primarily after the advent of agriculture, i.e., the Neolithic time, especially in East Asia, which was one of the centers of ancient agricultural civilization. To answer this question requires an accurate estimation of the time of lineage expansion as well as that of population expansion in a population sample without ascertainment bias. In this study, we analyzed all available mtDNA genomes of East Asians ascertained by random sampling, a total of 367 complete mtDNA sequences generated by the 1000 Genome Project, including 249 Chinese (CHB, CHD, and CHS) and 118 Japanese (JPT). We found that major mtDNA lineages underwent expansions, all of which, except for two JPT-specific lineages, including D4, D4b2b, D4a, D4j, D5a2a, A, N9a, F1a1'4, F2, B4, B4a, G2a1 and M7b1'2'4, occurred before 10 kya, i.e., before the Neolithic time (symbolized by Dadiwan Culture at 7.9 kya) in East Asia. Consistent to this observation, the further analysis showed that the population expansion in East Asia started at 13 kya and lasted until 4 kya. The results suggest that the population growth in East Asia constituted a need for the introduction of agriculture and might be one of the driving forces that led to the further development of agriculture.

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          Most cited references 53

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

             Robert Edgar (2004)
            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                6 October 2011
                : 6
                : 10
                Affiliations
                [1 ]State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
                [2 ]Chinese Academy of Sciences and Max-Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
                [3 ]Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China
                [4 ]Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America
                University of Cambridge, United Kingdom
                Author notes

                Conceived and designed the experiments: H-XZ SY LJ. Performed the experiments: H-XZ SY Z-DQ. Analyzed the data: H-XZ SY YW Z-DQ. Contributed reagents/materials/analysis tools: YW. Wrote the paper: H-XZ SY Z-DQ J-ZT HL LJ.

                Article
                PONE-D-11-09023
                10.1371/journal.pone.0025835
                3188578
                21998705
                Zheng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 7
                Categories
                Research Article
                Biology
                Genetics
                Human Genetics
                Population Biology
                Population Genetics
                Effective Population Size
                Social and Behavioral Sciences
                Anthropology
                Biological Anthropology

                Uncategorized

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