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FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models.

ABSTRACT: MOTIVATION:Species tree estimation is a basic part of biological research but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. All common approaches in phylogenomic studies either reduce available data or are error-prone, and thus, scalable methods that do not discard data and have high accuracy on large heterogeneous datasets are needed. RESULTS:We present FastMulRFS, a polynomial-time method for estimating species trees without knowledge of orthology. We prove that FastMulRFS is statistically consistent under a generic model of GDL when adversarial GDL does not occur. Our extensive simulation study shows that FastMulRFS matches the accuracy of MulRF (which tries to solve the same optimization problem) and has better accuracy than prior methods, including ASTRAL-multi (the only method to date that has been proven statistically consistent under GDL), while being much faster than both methods. AVAILABILITY AND IMPEMENTATION:FastMulRFS is available on Github (https://github.com/ekmolloy/fastmulrfs). SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Molloy EK

PROVIDER: S-EPMC7355287 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models.

Molloy Erin K EK Warnow Tandy T

Bioinformatics (Oxford, England) 20200701 Suppl_1

<h4>Motivation</h4>Species tree estimation is a basic part of biological research but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. All common approaches in phylogenomic studies either reduce available data or are error-prone, and thus, scalable methods that do not discard data and have high accuracy on large heterogeneous datasets are needed.<h4>Results</h4>We present FastMulRFS, a polynomial-time method f ...[more]

PMID: 32657396

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Project description:Whole-genome duplication (WGD) occurs broadly and repeatedly across the history of eukaryotes and is recognized as a prominent evolutionary force, especially in plants. Immediately following WGD, most genes are present in two copies as paralogs. Due to this redundancy, one copy of a paralog pair commonly undergoes pseudogenization and is eventually lost. When speciation occurs shortly after WGD; however, differential loss of paralogs may lead to spurious phylogenetic inference resulting from the inclusion of pseudoorthologs-paralogous genes mistakenly identified as orthologs because they are present in single copies within each sampled species. The influence and impact of including pseudoorthologs versus true orthologs as a result of gene extinction (or incomplete laboratory sampling) are only recently gaining empirical attention in the phylogenomics community. Moreover, few studies have yet to investigate this phenomenon in an explicit coalescent framework. Here, using mathematical models, numerous simulated data sets, and two newly assembled empirical data sets, we assess the effect of pseudoorthologs on species tree estimation under varying degrees of incomplete lineage sorting (ILS) and differential gene loss scenarios following WGD. When gene loss occurs along the terminal branches of the species tree, alignment-based (BPP) and gene-tree-based (ASTRAL, MP-EST, and STAR) coalescent methods are adversely affected as the degree of ILS increases. This can be greatly improved by sampling a sufficiently large number of genes. Under the same circumstances, however, concatenation methods consistently estimate incorrect species trees as the number of genes increases. Additionally, pseudoorthologs can greatly mislead species tree inference when gene loss occurs along the internal branches of the species tree. Here, both coalescent and concatenation methods yield inconsistent results. These results underscore the importance of understanding the influence of pseudoorthologs in the phylogenomics era. [Coalescent method; concatenation method; incomplete lineage sorting; pseudoorthologs; single-copy gene; whole-genome duplication.].

Project description:Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer such epidemiological parameters as the average number of secondary infections Re and the infectious time from a phylogenetic tree (a genealogy of pathogen sequences). The representatives of this model family focus on various aspects of pathogen epidemics. For instance, the birth-death exposed-infectious (BDEI) model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters. With constantly growing sequencing data, MTBD models should be extremely useful for unravelling information on pathogen epidemics. However, existing implementations of these models in a phylodynamic framework have not yet caught up with the sequencing speed. Computing time and numerical instability issues limit their applicability to medium data sets (≤ 500 samples), while the accuracy of estimations should increase with more data. We propose a new highly parallelizable formulation of ordinary differential equations for MTBD models. We also extend them to forests to represent situations when a (sub-)epidemic started from several cases (e.g., multiple introductions to a country). We implemented it for the BDEI model in a maximum likelihood framework using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in two minutes on a phylogenetic tree of 10,000 samples. Comparison to the existing implementations on simulated data shows that it is not only much faster but also more accurate. An application of our tool to the 2014 Ebola epidemic in Sierra-Leone is also convincing, with very fast calculation and precise estimates. As MTBD models are closely related to Cladogenetic State Speciation and Extinction (ClaSSE)-like models, our findings could also be easily transferred to the macroevolution domain.

Dataset Information

FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models.

Publications

FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models.

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