Publications

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1687 Publications visible to you, out of a total of 1687

Abstract

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Authors: T. J. Galvin, M. Huynh, R. P. Norris, X. R. Wang, E. Hopkins, O. I. Wong, S. Shabala, L. Rudnick, M. J. Alger, K. L. Polsterer

Date Published: 1st Oct 2019

Publication Type: Journal

Abstract

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Authors: Darach Watson, Camilla J. Hansen, Jonatan Selsing, Andreas Koch, Daniele B. Malesani, Anja C. Andersen, Johan P. U. Fynbo, Almudena Arcones, Andreas Bauswein, Stefano Covino, Aniello Grado, Kasper E. Heintz, Leslie Hunt, Chryssa Kouveliotou, Giorgos Leloudas, Andrew J. Levan, Paolo Mazzali, Elena Pian

Date Published: 1st Oct 2019

Publication Type: Journal

Abstract

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Authors: L. Galbany, C. Ashall, P. Höflich, S. González-Gaitán, S. Taubenberger, M. Stritzinger, E. Y. Hsiao, P. Mazzali, E. Baron, S. Blondin, S. Bose, M. Bulla, J. F. Burke, C. R. Burns, R. Cartier, P. Chen, M. Della Valle, T. R. Diamond, C. P. Gutiérrez, J. Harmanen, D. Hiramatsu, T. W.-S. Holoien, G. Hosseinzadeh, D. Andrew Howell, Y. Huang, C. Inserra, T. de Jaeger, S. W. Jha, T. Kangas, M. Kromer, J. D. Lyman, K. Maguire, G. Howie Marion, D. Milisavljevic, S. J. Prentice, A. Razza, T. M. Reynolds, D. J. Sand, B. J. Shappee, R. Shekhar, S. J. Smartt, K. G. Stassun, M. Sullivan, S. Valenti, S. Villanueva, X. Wang, J. Craig Wheeler, Q. Zhai, J. Zhang

Date Published: 1st Oct 2019

Publication Type: Journal

Abstract

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Author: Erica Hopkins

Date Published: 1st Oct 2019

Publication Type: Master's Thesis

Abstract (Expand)

Inferring gene trees is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges species tree-aware methods seek to use information from a putative species tree. However, there are few methods available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data pre-processing (e.g., computing bootstrap trees), and rely on approximations and heuristics that limit the exploration of tree space. Here we present GeneRax, the first maximum likelihood species tree-aware gene tree inference software. It simultaneously accounts for substitutions at the sequence level and gene level events, such as duplication, transfer and loss and uses established maximum likelihood optimization algorithms. GeneRax can infer rooted gene trees for an arbitrary number of gene families, directly from the per-gene sequence alignments and a rooted, but undated, species tree. We show that compared to competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms relative Robinson-Foulds distance. While, on empirical datasets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and that it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1099 Cyanobacteria families in eight minutes on 512 CPU cores. Thus, its advanced parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax.

Authors: Benoit Morel, Alexey M. Kozlov, Alexandros Stamatakis, Gergely J. Szöllősi

Date Published: 26th Sep 2019

Publication Type: Journal

Abstract

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Author: Ariane Nunes-Alves

Date Published: 16th Sep 2019

Publication Type: Journal

Abstract

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Authors: Samuel Jones, Benoit Côté, Friedrich K. Röpke, Shinya Wanajo

Date Published: 10th Sep 2019

Publication Type: Journal

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