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      An ancestral recombination graph of human, Neanderthal, and Denisovan genomes

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          Abstract

          A new heuristic ARG inference tool maps archaic hominin admixture and highlights genomic regions unique to modern humans.

          Abstract

          Many humans carry genes from Neanderthals, a legacy of past admixture. Existing methods detect this archaic hominin ancestry within human genomes using patterns of linkage disequilibrium or direct comparison to Neanderthal genomes. Each of these methods is limited in sensitivity and scalability. We describe a new ancestral recombination graph inference algorithm that scales to large genome-wide datasets and demonstrate its accuracy on real and simulated data. We then generate a genome-wide ancestral recombination graph including human and archaic hominin genomes. From this, we generate a map within human genomes of archaic ancestry and of genomic regions not shared with archaic hominins either by admixture or incomplete lineage sorting. We find that only 1.5 to 7% of the modern human genome is uniquely human. We also find evidence of multiple bursts of adaptive changes specific to modern humans within the past 600,000 years involving genes related to brain development and function.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              A global reference for human genetic variation

              The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                July 2021
                16 July 2021
                : 7
                : 29
                : eabc0776
                Affiliations
                [1 ]Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
                [2 ]Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
                [3 ]Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
                [4 ]Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
                Author notes
                [* ]Corresponding author. Email: ed@ 123456soe.ucsc.edu
                [†]

                Present address: The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA.

                Author information
                http://orcid.org/0000-0001-5881-7329
                http://orcid.org/0000-0002-2733-7776
                http://orcid.org/0000-0003-0516-5827
                Article
                abc0776
                10.1126/sciadv.abc0776
                8284891
                34272242
                da9c182a-bfe0-42c3-8a6b-9f99d62b2166
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 04 April 2020
                : 03 June 2021
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1754451
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32 HG00834
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: HG00834
                Funded by: doi http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Funded by: doi http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: DEB-1754451
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Anthropology
                Evolutionary Biology
                Genetics
                Custom metadata
                Karla Peñamante

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