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2020 | OriginalPaper | Buchkapitel

Polynomial-Time Statistical Estimation of Species Trees Under Gene Duplication and Loss

verfasst von : Brandon Legried, Erin K. Molloy, Tandy Warnow, Sébastien Roch

Erschienen in: Research in Computational Molecular Biology

Verlag: Springer International Publishing

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Abstract

Phylogenomics—the estimation of species trees from multi-locus datasets—is a common step in many biological studies. However, this estimation is challenged by the fact that genes can evolve under processes, including incomplete lineage sorting (ILS) and gene duplication and loss (GDL), that make their trees different from the species tree. In this paper, we address the challenge of estimating the species tree under GDL. We show that species trees are identifiable under a standard stochastic model for GDL, and that the polynomial-time algorithm ASTRAL-multi, a recent development in the ASTRAL suite of methods, is statistically consistent under this GDL model. We also provide a simulation study evaluating ASTRAL-multi for species tree estimation under GDL. All scripts and datasets used in this study are available on the Illinois Data Bank: https://doi.org/10.13012/B2IDB-2626814_V1.

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Vorheriges Kapitel AStarix: Fast and Optimal Sequence-to-Graph Alignment

Nächstes Kapitel RoboCOP: Multivariate State Space Model Integrating Epigenomic Accessibility Data to Elucidate Genome-Wide Chromatin Occupancy

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Titel: Polynomial-Time Statistical Estimation of Species Trees Under Gene Duplication and Loss
verfasst von: Brandon Legried
Erin K. Molloy
Tandy Warnow
Sébastien Roch
Verlag: Springer International Publishing
Buch: Research in Computational Molecular Biology
Print ISBN: 978-3-030-45256-8

Electronic ISBN: 978-3-030-45257-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-45257-5_8

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