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      Genomic analysis of snub-nosed monkeys (Rhinopithecus) identifies genes and processes related to high-altitude adaptation.

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          Abstract

          The snub-nosed monkey genus Rhinopithecus includes five closely related species distributed across altitudinal gradients from 800 to 4,500 m. Rhinopithecus bieti, Rhinopithecus roxellana, and Rhinopithecus strykeri inhabit high-altitude habitats, whereas Rhinopithecus brelichi and Rhinopithecus avunculus inhabit lowland regions. We report the de novo whole-genome sequence of R. bieti and genomic sequences for the four other species. Eight shared substitutions were found in six genes related to lung function, DNA repair, and angiogenesis in the high-altitude snub-nosed monkeys. Functional assays showed that the high-altitude variant of CDT1 (Ala537Val) renders cells more resistant to UV irradiation, and the high-altitude variants of RNASE4 (Asn89Lys and Thr128Ile) confer enhanced ability to induce endothelial tube formation in vitro. Genomic scans in the R. bieti and R. roxellana populations identified signatures of selection between and within populations at genes involved in functions relevant to high-altitude adaptation. These results provide valuable insights into the adaptation to high altitude in the snub-nosed monkeys.

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          Most cited references45

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          ASTRAL: genome-scale coalescent-based species tree estimation

          Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            An algorithm for progressive multiple alignment of sequences with insertions.

            Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. For more than a few sequences, exact algorithms become computationally impractical, and progressive algorithms iterating pairwise alignments are widely used. These heuristic methods have a serious drawback because pairwise algorithms do not differentiate insertions from deletions and end up penalizing single insertion events multiple times. Such an unrealistically high penalty for insertions typically results in overmatching of sequences and an underestimation of the number of insertion events. We describe a modification of the traditional alignment algorithm that can distinguish insertion from deletion and avoid repeated penalization of insertions and illustrate this method with a pair hidden Markov model that uses an evolutionary scoring function. In comparison with a traditional progressive alignment method, our algorithm infers a greater number of insertion events and creates gaps that are phylogenetically consistent but spatially less concentrated. Our results suggest that some insertion/deletion "hot spots" may actually be artifacts of traditional alignment algorithms.
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              Revising the human mutation rate: implications for understanding human evolution.

              It is now possible to make direct measurements of the mutation rate in modern humans using next-generation sequencing. These measurements reveal a value that is approximately half of that previously derived from fossil calibration, and this has implications for our understanding of demographic events in human evolution and other aspects of population genetics. Here, we discuss the implications of a lower-than-expected mutation rate in relation to the timescale of human evolution.
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                Author and article information

                Journal
                Nat. Genet.
                Nature genetics
                Springer Nature
                1546-1718
                1061-4036
                Aug 2016
                : 48
                : 8
                Affiliations
                [1 ] State Key Laboratory for Conservation and Utilization of Bio-resource in Yunnan, Yunnan University, Kunming, China.
                [2 ] State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
                [3 ] Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
                [4 ] Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
                [5 ] Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China.
                [6 ] Deparment of Laboratory Animal Science, Kunming Medical University, Kunming, China.
                [7 ] Key Laboratory for Animal Genetic Diversity and Evolution of High Education in Yunnan Province, School of Life Sciences, Yunnan University, Kunming, China.
                [8 ] Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China.
                [9 ] College of Life Sciences, Northwest University, Xi'an, China.
                [10 ] Institute of Zoology, Shaanxi Academy of Sciences, Xi'an, China.
                [11 ] Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing, China.
                [12 ] Fanjing Mountain National Nature Reserve, Guizhou, China.
                [13 ] Institue of Primatology and Human Evolution, Sun Yat-Sen University, Guangzhou, China.
                [14 ] Nujiang Prefecture Forestry Bureau, Yunnan, China.
                [15 ] Shennongjia National Nature Reserve, Hubei, China.
                [16 ] Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
                [17 ] San Diego Zoo Institute for Conservation Research, Escondido, California, USA.
                [18 ] Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Ya'an, China.
                [19 ] Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA.
                Article
                ng.3615
                10.1038/ng.3615
                27399969
                1cf9a7e6-61df-468f-9e22-d94384ceea33
                History

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