Fast algorithms and heuristics for phylogenomics under ILS and hybridization

Yun, Yu; Ristic, Nikola; Nakhleh, Luay

doi:10.1186/1471-2105-14-s15-s6

Cited by 41 publications

(41 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where τ Ψ ðbÞ for edge b = ðx; yÞ is the time of node x in the phylogenetic network Ψ. A full derivation of the formula and a more efficient algorithm for computing it along the lines of Yu et al (17), which avoid explicit summations over the possible coalescent histories, are given in SI Appendix.…”

Section: Resultsmentioning

confidence: 99%

“…A major direction for future research is scaling up our methods to larger datasets. Currently, it takes a few seconds to a few minutes to evaluate the likelihood of a phylogenetic network with 10-20 taxa (17). This running time can vary significantly even among networks with the same numbers of taxa and reticulation events, because the shape of the gene tree and the configuration of the reticulation nodes in the network (their locations and interdependencies) are the crucial factors (27).…”

Section: Discussionmentioning

confidence: 99%

“…2 in this work. Using this formulation, the pmf pðG i jΨ; ΓÞ, when G i is the gene genealogy's topology alone, is computable using the algorithms of Yu et al (16,17). In Results, we derive the pdf of gene genealogies (with branch lengths), given a phylogenetic network.…”

Section: Modelmentioning

confidence: 99%

“…However, these methods are all applicable to simple scenarios of species evolution and mostly assume a known hypothesis about the topology of the phylogenetic network. As reported (16,17), we devised methods for computing the likelihood of a phylogenetic network, given a set of gene tree topologies. Still, these methods did not allow for inference of phylogenetic networks (they assume a given phylogenetic network topology and compute its likelihood).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Maximum likelihood inference of reticulate evolutionary histories

Yu¹,

Dong²,

Liu

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

264

389

View full text Add to dashboard Cite

Hybridization plays an important role in the evolution of certain groups of organisms, adaptation to their environments, and diversification of their genomes. The evolutionary histories of such groups are reticulate, and methods for reconstructing them are still in their infancy and have limited applicability. We present a maximum likelihood method for inferring reticulate evolutionary histories while accounting simultaneously for incomplete lineage sorting. Additionally, we propose methods for assessing confidence in the amount of reticulation and the topology of the inferred evolutionary history. Our method obtains accurate estimates of reticulate evolutionary histories on simulated datasets. Furthermore, our method provides support for a hypothesis of a reticulate evolutionary history inferred from a set of house mouse (Mus musculus) genomes. As evidence of hybridization in eukaryotic groups accumulates, it is essential to have methods that infer reticulate evolutionary histories. The work we present here allows for such inference and provides a significant step toward putting phylogenetic networks on par with phylogenetic trees as a model of capturing evolutionary relationships. reticulate evolution | incomplete lineage sorting | phylogenetic networks | maximum likelihood P hylogenetic trees have long been a mainstay of biology, providing an interpretive model of the evolution of molecules and characters and a backdrop against which comparative genomics and phenomics are conducted. Nevertheless, some evolutionary events, most notably horizontal gene transfer in prokaryotes and hybridization in eukaryotes, necessitate going beyond trees (1). These events result in reticulate evolutionary histories, which are best modeled by phylogenetic networks (2). The topology of a phylogenetic network is given by a rooted, directed, acyclic graph (rDAG) that is leaf-labeled by a set of taxa ( Fig. 1; more details are provided in Model and SI Appendix). Reticulation events result in genomic regions with local genealogies that are incongruent with the speciation pattern. Several methods and heuristics use this incongruence as a signal for inferring reticulation events and reconstructing phylogenetic networks from local genealogies. These methods, which are surveyed elsewhere (2-4), assume that reticulation events are the sole cause of all incongruence among the gene trees and seek phylogenetic networks to explain all of the incongruence. A serious limitation of these methods is that they would grossly overestimate the amount of reticulation in a dataset when other causes of incongruence are at play. Indeed, several recent studies (5-9) have shown that detecting hybridization in practice can be complicated by the presence of incomplete lineage sorting (ILS) (Fig. 1).Recently, a set of methods was devised to analyze data where reticulation and ILS might both be simultaneously at play (10-15). However, these methods are all applicable to simple scenarios of species evolution and mostly assume a known hypothesis about the topol...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Maximum likelihood inference of reticulate evolutionary histories

Yu¹,

Dong²,

Liu

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

264

389

View full text Add to dashboard Cite

show abstract

“…The reason for this is that 184 when partial likelihood vectors are split (described below), those become symbolic terms 185 that do not evaluate to partial likelihoods until they are merged later. This is analogous 186 to the difference between ancestral configurations on species trees [31] and their labeled 187 counterparts on phylogenetic networks [32], where the latter are in many cases just 188 symbolic terms that do not evaluate to true (partial) likelihood values.…”

mentioning

confidence: 99%

Bayesian Inference Of Phylogenetic Networks From Bi-allelic Genetic Markers

Zhu

Wen

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

10Phylogenetic networks are rooted, directed, acyclic graphs that model reticulate sample the posterior of phylogenetic networks given bi-allelic marker data. Our method 27 has a very good performance in terms of accuracy and robustness as we demonstrate on 28 simulated data, as well as a data set of multiple New Zealand species of the plant genus 29 Ourisia (Plantaginaceae). We implemented the method in the publicly available, 30 open-source PhyloNet software package. 31Author summary 32The availability of genomic data has revolutionized the study of evolutionary histories 33 and phylogeny inference. Inferring evolutionary histories from genomic data requires, in 34 most cases, accounting for the fact that different genomic regions could have 35 evolutionary histories that differ from each other as well as from that of the species 36 from which the genomes were sampled. In this paper, we introduce a method for 37 inferring evolutionary histories while accounting for two processes that could give rise to 38 such differences across the genomes, namely incomplete lineage sorting and 39 hybridization. We introduce a novel algorithm for computing the likelihood of 40 phylogenetic networks from bi-allelic genetic markers and use it in a Bayesian inference 41 method. Analyses of synthetic and empirical data sets show a very good performance of 42 the method in terms of the estimates it obtains.

show abstract

Estimation of Species Trees

Mallo

Martins

Posada

2014

Encyclopedia of Life Sciences

View full text Add to dashboard Cite

During the last decades, gene trees have been often interpreted as species phylogenies. However, the extensive gene tree discordance found in multi‐locus datasets has put into question this interpretation, and a variety of new methods that explicitly consider species trees have been proposed in recent years. Some of these explicitly consider evolutionary processes that can lead to true gene tree discordance, namely incomplete lineage sorting, gene duplication and loss and horizontal gene transfer. Choosing the most appropriate species tree method for the data at hand is not straightforward due to different data prerequisites, model assumptions, analytical strategies and computational implementations. Key Concepts: We could think of at least three different phylogenetic layers corresponding to species trees, locus trees and gene trees. These depict, respectively, the history of the sampled species, loci and genes copies. Traditional phylogenetic inference has focused on the reconstruction of gene trees, assumed to be accurate proxies for species history. True species, locus and gene trees can be incongruent due to the effect of evolutionary processes like incomplete lineage sorting, gene duplication and loss and horizontal gene transfer. This incongruence might appear larger due to estimation error. Extensive gene tree incongruence unveiled in multi‐locus datasets has encouraged the development of methods that explicitly reconstruct species trees. The supermatrix approach combines loci into a super‐alignment and estimates the corresponding supergene tree. Supertree methods combine gene trees to obtain an estimate of the species tree. Other methods co‐estimate gene and species trees in a single step using full probabilistic models. Different species tree methods require distinct data specifications, mainly related with the consideration of paralogs, number of sampled species, missing taxa and number of loci. Rooting and reconstruction uncertainty must be carefully considered before choosing a species tree method.

show abstract

Fast algorithms and heuristics for phylogenomics under ILS and hybridization

Cited by 41 publications

References 28 publications

Maximum likelihood inference of reticulate evolutionary histories

Maximum likelihood inference of reticulate evolutionary histories

Bayesian Inference Of Phylogenetic Networks From Bi-allelic Genetic Markers

Estimation of Species Trees

Contact Info

Product

Resources

About