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      Phylogenetic Inference of HIV Transmission Clusters

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          Abstract

          Better understanding the structure and dynamics of HIV transmission networks is essential for designing the most efficient interventions to prevent new HIV transmissions, and ultimately for gaining control of the HIV epidemic. The inference of phylogenetic relationships and the interpretation of results rely on the definition of the HIV transmission cluster. The definition of the HIV cluster is complex and dependent on multiple factors, including the design of sampling, accuracy of sequencing, precision of sequence alignment, evolutionary models, the phylogenetic method of inference, and specified thresholds for cluster support. While the majority of studies focus on clusters, non-clustered cases could also be highly informative. A new dimension in the analysis of the global and local HIV epidemics is the concept of phylogenetically distinct HIV sub-epidemics. The identification of active HIV sub-epidemics reveals spreading viral lineages and may help in the design of targeted interventions. HIV clustering can also be affected by sampling density. Obtaining a proper sampling density may increase statistical power and reduce sampling bias, so sampling density should be taken into account in study design and in interpretation of phylogenetic results. Finally, recent advances in long-range genotyping may enable more accurate inference of HIV transmission networks. If performed in real time, it could both inform public-health strategies and be clinically relevant (e.g., drug-resistance testing).

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          Most cited references68

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          Inferring ancient divergences requires genes with strong phylogenetic signals.

          To tackle incongruence, the topological conflict between different gene trees, phylogenomic studies couple concatenation with practices such as rogue taxon removal or the use of slowly evolving genes. Phylogenomic analysis of 1,070 orthologues from 23 yeast genomes identified 1,070 distinct gene trees, which were all incongruent with the phylogeny inferred from concatenation. Incongruence severity increased for shorter internodes located deeper in the phylogeny. Notably, whereas most practices had little or negative impact on the yeast phylogeny, the use of genes or internodes with high average internode support significantly improved the robustness of inference. We obtained similar results in analyses of vertebrate and metazoan phylogenomic data sets. These results question the exclusive reliance on concatenation and associated practices, and argue that selecting genes with strong phylogenetic signals and demonstrating the absence of significant incongruence are essential for accurately reconstructing ancient divergences.
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            Statistical tests of models of DNA substitution.

            Penny et al. have written that "The most fundamental criterion for a scientific method is that the data must, in principle, be able to reject the model. Hardly any [phylogenetic] tree-reconstruction methods meet this simple requirement." The ability to reject models is of such great importance because the results of all phylogenetic analyses depend on their underlying models--to have confidence in the inferences, it is necessary to have confidence in the models. In this paper, a test statistic suggested by Cox is employed to test the adequacy of some statistical models of DNA sequence evolution used in the phylogenetic inference method introduced by Felsenstein. Monte Carlo simulations are used to assess significance levels. The resulting statistical tests provide an objective and very general assessment of all the components of a DNA substitution model; more specific versions of the test are devised to test individual components of a model. In all cases, the new analyses have the additional advantage that values of phylogenetic parameters do not have to be assumed in order to perform the tests.
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              Novel information theory-based measures for quantifying incongruence among phylogenetic trees.

              Phylogenies inferred from different data matrices often conflict with each other necessitating the development of measures that quantify this incongruence. Here, we introduce novel measures that use information theory to quantify the degree of conflict or incongruence among all nontrivial bipartitions present in a set of trees. The first measure, internode certainty (IC), calculates the degree of certainty for a given internode by considering the frequency of the bipartition defined by the internode (internal branch) in a given set of trees jointly with that of the most prevalent conflicting bipartition in the same tree set. The second measure, IC All (ICA), calculates the degree of certainty for a given internode by considering the frequency of the bipartition defined by the internode in a given set of trees in conjunction with that of all conflicting bipartitions in the same underlying tree set. Finally, the tree certainty (TC) and TC All (TCA) measures are the sum of IC and ICA values across all internodes of a phylogeny, respectively. IC, ICA, TC, and TCA can be calculated from different types of data that contain nontrivial bipartitions, including from bootstrap replicate trees to gene trees or individual characters. Given a set of phylogenetic trees, the IC and ICA values of a given internode reflect its specific degree of incongruence, and the TC and TCA values describe the global degree of incongruence between trees in the set. All four measures are implemented and freely available in version 8.0.0 and subsequent versions of the widely used program RAxML.
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                Author and article information

                Contributors
                Journal
                Infectious Diseases and Translational Medicine
                Infect. Dis. Transl. Med.
                Infect. Dis. Transl. Med.
                International Biological and Medical Journals Publishing House Co., Limited (Room E16, 3/f, Yongda Commercial Building, No.97, Bonham Stand (Sheung Wan), HongKong )
                2411-2917
                31 October 2017
                31 October 2017
                : 3
                : 2
                : 51-59 (pp. )
                Affiliations
                From Department of Immunology & Infectious Diseases, Harvard School of Public Health, United States.
                From Department of Immunology & Infectious Diseases, Harvard School of Public Health, United States.
                From Department of Immunology & Infectious Diseases, Harvard School of Public Health, United States.
                Author notes
                Correspondence to: Vlad Novitsky, Email: vnovi@hsph.harvard.edu.
                Article
                10.11979/idtm.201702007
                7635ab62-6516-4679-82b1-4dc8fbe742a9

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Page count
                Figures: 1, Tables: 0, References: 101, Pages: 9
                Product
                Categories
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                Medicine,Infectious disease & Microbiology
                Sampling density,HIV transmission network,HIV transmission clusters,HIV sub-epidemics,Long-range HIV genotyping

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