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      A machine-compiled microbial supertree from figure-mining thousands of papers

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      Research Ideas and Outcomes
      Pensoft Publishers

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          Comparison of phylogenetic trees

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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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              A fast parallel algorithm for thinning digital patterns

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                Author and article information

                Journal
                Research Ideas and Outcomes
                RIO
                Pensoft Publishers
                2367-7163
                May 09 2017
                May 09 2017
                : 3
                : e13589
                Article
                10.3897/rio.3.e13589
                50b19085-cd6e-4591-8193-e59b929baab4
                © 2017

                http://creativecommons.org/licenses/by/4.0/

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