Polynomial-time statistical estimation of species trees under gene duplication and loss

Brandon Legried; Erin K. Molloy; Tandy Warnow; Sébastien Roch

doi:10.1007/978-3-030-45257-5_8

Polynomial-time statistical estimation of species trees under gene duplication and loss

Brandon Legried, Erin K. Molloy, Tandy Warnow, Sébastien Roch

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
Editors	Russell Schwartz
Publisher	Springer
Pages	120-135
Number of pages	16
ISBN (Print)	9783030452568
DOIs	https://doi.org/10.1007/978-3-030-45257-5_8
State	Published - 2020
Event	24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy Duration: May 10 2020 → May 13 2020

Publication series

Name	Lecture Notes in Computer Science
Volume	12074 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020
Country/Territory	Italy
City	Padua
Period	5/10/20 → 5/13/20

Keywords

ASTRAL
Estimation
Gene duplication and loss
Identifiability
Species trees
Statistical consistency

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Online availability

10.1007/978-3-030-45257-5_8

Library availability

Discover UIUC Full Text

Cite this

Legried, B., Molloy, E. K., Warnow, T., & Roch, S. (2020). Polynomial-time statistical estimation of species trees under gene duplication and loss. In R. Schwartz (Ed.), Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings (pp. 120-135). (Lecture Notes in Computer Science; Vol. 12074 LNBI). Springer. https://doi.org/10.1007/978-3-030-45257-5_8

Polynomial-time statistical estimation of species trees under gene duplication and loss. / Legried, Brandon; Molloy, Erin K.; Warnow, Tandy et al.
Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings. ed. / Russell Schwartz. Springer, 2020. p. 120-135 (Lecture Notes in Computer Science; Vol. 12074 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Legried, B, Molloy, EK, Warnow, T & Roch, S 2020, Polynomial-time statistical estimation of species trees under gene duplication and loss. in R Schwartz (ed.), Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings. Lecture Notes in Computer Science, vol. 12074 LNBI, Springer, pp. 120-135, 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020, Padua, Italy, 5/10/20. https://doi.org/10.1007/978-3-030-45257-5_8

@inproceedings{ddb5618463c04e6cbd40ccf64166c13c,

title = "Polynomial-time statistical estimation of species trees under gene duplication and loss",

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.",

keywords = "ASTRAL, Estimation, Gene duplication and loss, Identifiability, Species trees, Statistical consistency",

author = "Brandon Legried and Molloy, {Erin K.} and Tandy Warnow and S{\'e}bastien Roch",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 ; Conference date: 10-05-2020 Through 13-05-2020",

year = "2020",

doi = "10.1007/978-3-030-45257-5_8",

language = "English (US)",

isbn = "9783030452568",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "120--135",

editor = "Russell Schwartz",

booktitle = "Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings",

address = "Germany",

}

TY - GEN

T1 - Polynomial-time statistical estimation of species trees under gene duplication and loss

AU - Legried, Brandon

AU - Molloy, Erin K.

AU - Warnow, Tandy

AU - Roch, Sébastien

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - ASTRAL

KW - Estimation

KW - Gene duplication and loss

KW - Identifiability

KW - Species trees

KW - Statistical consistency

UR - http://www.scopus.com/inward/record.url?scp=85084260667&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85084260667&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-45257-5_8

DO - 10.1007/978-3-030-45257-5_8

M3 - Conference contribution

AN - SCOPUS:85084260667

SN - 9783030452568

T3 - Lecture Notes in Computer Science

SP - 120

EP - 135

BT - Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings

A2 - Schwartz, Russell

PB - Springer

T2 - 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

Y2 - 10 May 2020 through 13 May 2020

ER -

Polynomial-time statistical estimation of species trees under gene duplication and loss

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Data from: Polynomial-Time Statistical Estimation of Species Trees under Gene Duplication and Loss

Cite this

Polynomial-time statistical estimation of species trees under gene duplication and loss

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Datasets

Data from: Polynomial-Time Statistical Estimation of Species Trees under Gene Duplication and Loss

Cite this