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      Highly divergent lineage of narrow-headed vole from the Late Pleistocene Europe

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          Abstract

          During the Late Pleistocene, narrow-headed voles ( Lasiopodomys gregalis) inhabited Eurasia’s vast territories, frequently becoming the dominant small mammal species among steppe-tundra communities. We investigated the relationship between this species’ European and Asiatic populations by sequencing the mtDNA genomes of two extant specimens from Russia and 10 individuals from five Central European sites, dated to the post-LGM period. Phylogenetic analyses based on a large portion of mtDNA genomes highly supported the positioning of L. gregalis within Arvicolinae. The phylogeny based on mtDNA cytochrome b sequences revealed a deep divergence of European narrow-headed voles from Asiatic ones and their sister position against the extant L. gregalis and L. raddei. The divergence of the European lineage was estimated to a minimum 230 thousand years ago. This suggest, contrary to the current biogeographic hypotheses, that during the interglacial periods narrow-headed vole did not retreat from Europe but survived the unfavourable conditions within the refugial areas. Based on this result, we propose to establish a cryptic species status for the Late Pleistocene European narrow-headed vole and to name this taxon Lasiopodomys anglicus.

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          AdapterRemoval v2: rapid adapter trimming, identification, and read merging

          Background As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run. Findings We introduce AdapterRemoval v2, a major revision of AdapterRemoval v1, which introduces (i) striking improvements in throughput, through the use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support, (ii) the ability to handle datasets containing reads or read-pairs with different adapters or adapter pairs, (iii) simultaneous demultiplexing and adapter trimming, (iv) the ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets, and (v) native gzip and bzip2 support. Conclusions We show that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations. Electronic supplementary material The online version of this article (doi:10.1186/s13104-016-1900-2) contains supplementary material, which is available to authorized users.
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            Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

            Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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              Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.

              Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we present a generalization of the GMRF model that allows for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA). We analyze a multilocus alignment of HIV-1 CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA data set.
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                Author and article information

                Contributors
                nadachowski@isez.pan.krakow.pl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 November 2019
                28 November 2019
                2019
                : 9
                : 17799
                Affiliations
                [1 ]ISNI 0000 0004 1937 1290, GRID grid.12847.38, Centre of New Technologies, , University of Warsaw, ; Banacha 2c, 02-097 Warsaw, Poland
                [2 ]ISNI 0000 0001 0940 8692, GRID grid.460455.6, Institute of Systematics and Evolution of Animals, Polish Academy of Sciences, ; Sławkowska 17, 31-016 Krakow, Poland
                [3 ]ISNI 0000 0004 1937 116X, GRID grid.4491.8, Department of Zoology, , Charles University, ; Viničná 7, 128 44 Prague, Czech Republic
                Article
                53937
                10.1038/s41598-019-53937-1
                6882798
                31780683
                d2eeb28f-e883-4ab2-bc01-7451519789dc
                © The Author(s) 2019

                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
                : 2 August 2019
                : 1 October 2019
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                © The Author(s) 2019

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                phylogenetics,biogeography
                Uncategorized
                phylogenetics, biogeography

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