DISCO: Species Tree Inference using Multicopy Gene Family Tree Decomposition. (27th August 2021)
- Record Type:
- Journal Article
- Title:
- DISCO: Species Tree Inference using Multicopy Gene Family Tree Decomposition. (27th August 2021)
- Main Title:
- DISCO: Species Tree Inference using Multicopy Gene Family Tree Decomposition
- Authors:
- Willson, James
Roddur, Mrinmoy Saha
Liu, Baqiao
Zaharias, Paul
Warnow, Tandy - Editors:
- Hahn, Matthew
- Abstract:
- Abstract: Species tree inference from gene family trees is a significant problem in computational biology. However, gene tree heterogeneity, which can be caused by several factors including gene duplication and loss, makes the estimation of species trees very challenging. While there have been several species tree estimation methods introduced in recent years to specifically address gene tree heterogeneity due to gene duplication and loss (such as DupTree, FastMulRFS, ASTRAL-Pro, and SpeciesRax), many incur high cost in terms of both running time and memory. We introduce a new approach, DISCO, that decomposes the multi-copy gene family trees into many single copy trees, which allows for methods previously designed for species tree inference in a single copy gene tree context to be used. We prove that using DISCO with ASTRAL (i.e., ASTRAL-DISCO) is statistically consistent under the GDL model, provided that ASTRAL-Pro correctly roots and tags each gene family tree. We evaluate DISCO paired with different methods for estimating species trees from single copy genes (e.g., ASTRAL, ASTRID, and IQ-TREE) under a wide range of model conditions, and establish that high accuracy can be obtained even when ASTRAL-Pro is not able to correctly roots and tags the gene family trees. We also compare results using MI, an alternative decomposition strategy from Yang Y. and Smith S.A. (2014), and find that DISCO provides better accuracy, most likely as a result of covering more of the geneAbstract: Species tree inference from gene family trees is a significant problem in computational biology. However, gene tree heterogeneity, which can be caused by several factors including gene duplication and loss, makes the estimation of species trees very challenging. While there have been several species tree estimation methods introduced in recent years to specifically address gene tree heterogeneity due to gene duplication and loss (such as DupTree, FastMulRFS, ASTRAL-Pro, and SpeciesRax), many incur high cost in terms of both running time and memory. We introduce a new approach, DISCO, that decomposes the multi-copy gene family trees into many single copy trees, which allows for methods previously designed for species tree inference in a single copy gene tree context to be used. We prove that using DISCO with ASTRAL (i.e., ASTRAL-DISCO) is statistically consistent under the GDL model, provided that ASTRAL-Pro correctly roots and tags each gene family tree. We evaluate DISCO paired with different methods for estimating species trees from single copy genes (e.g., ASTRAL, ASTRID, and IQ-TREE) under a wide range of model conditions, and establish that high accuracy can be obtained even when ASTRAL-Pro is not able to correctly roots and tags the gene family trees. We also compare results using MI, an alternative decomposition strategy from Yang Y. and Smith S.A. (2014), and find that DISCO provides better accuracy, most likely as a result of covering more of the gene family tree leafset in the output decomposition. [Concatenation analysis; gene duplication and loss; species tree inference; summary method.] … (more)
- Is Part Of:
- Systematic biology. Volume 71:Number 3(2022)
- Journal:
- Systematic biology
- Issue:
- Volume 71:Number 3(2022)
- Issue Display:
- Volume 71, Issue 3 (2022)
- Year:
- 2022
- Volume:
- 71
- Issue:
- 3
- Issue Sort Value:
- 2022-0071-0003-0000
- Page Start:
- 610
- Page End:
- 629
- Publication Date:
- 2021-08-27
- Subjects:
- Biology -- Classification -- Periodicals
Biology -- Periodicals
Biologie -- Classification -- Périodiques
Biologie -- Périodiques
578.012 - Journal URLs:
- http://ukcatalogue.oup.com/ ↗
- DOI:
- 10.1093/sysbio/syab070 ↗
- Languages:
- English
- ISSNs:
- 1063-5157
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8589.180700
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21290.xml