2019
DOI: 10.1093/sysbio/syz051
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GHOST: Recovering Historical Signal from Heterotachously Evolved Sequence Alignments

Abstract: Molecular sequence data that have evolved under the influence of heterotachous evolutionary processes are known to mislead phylogenetic inference. We introduce the General Heterogeneous evolution On a Single Topology (GHOST) model of sequence evolution, implemented under a maximum-likelihood framework in the phylogenetic program IQ-TREE (http://www.iqtree.org). Simulations show that using the GHOST model, IQ-TREE can accurately recover the tree topology, branch lengths, and substitution model parameters from h… Show more

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Cited by 116 publications
(130 citation statements)
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“…For efficiency, we restricted the analyses to a subset of models (“‐mset LG” for amino acids, “‐mset GTR” for nucleotides) and employed the relaxed hierarchical clustering algorithm “‐rcluster 10” (Lanfear, Calcott, Kainer, Mayer, & Stamatakis, 2014). Heterotachy ML analyses used the GHOST (General Heterogeneous evolution On a Single Topology, Crotty et al, 2019) model to address rate variation across sites and lineages (Lopez, Casane, & Philippe, 2002). Node support values were calculated using 1,000 sh‐alrt replicates (Guindon et al, 2010) and 1,000 ultrafast bootstraps (Hoang, Chernomor, von Haeseler, Minh, & Vinh, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…For efficiency, we restricted the analyses to a subset of models (“‐mset LG” for amino acids, “‐mset GTR” for nucleotides) and employed the relaxed hierarchical clustering algorithm “‐rcluster 10” (Lanfear, Calcott, Kainer, Mayer, & Stamatakis, 2014). Heterotachy ML analyses used the GHOST (General Heterogeneous evolution On a Single Topology, Crotty et al, 2019) model to address rate variation across sites and lineages (Lopez, Casane, & Philippe, 2002). Node support values were calculated using 1,000 sh‐alrt replicates (Guindon et al, 2010) and 1,000 ultrafast bootstraps (Hoang, Chernomor, von Haeseler, Minh, & Vinh, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…IQ-TREE 2 offers a number of advanced models for phylogenomic data including partitioned models (Lanfear et al 2012;Chernomor et al 2016), mixture models (Le et al 2008;Le and Gascuel 2010;Le et al 2012), posterior-mean site frequency models (Wang et al 2017), and heterotachy models (Crotty et al 2019). For allele frequency data IQ-TREE 2 implements the polymorphism-aware models (Schrempf et al 2016;Schrempf et al 2019).…”
Section: Time-reversible Models Of Sequence Evolutionmentioning
confidence: 99%
“…Unlike the trees of the 3-class model, the trees of the 10-class model (Supplementary Figure S3) preferred by AIC bear the hallmarks of overfitting, as described in Crotty et al (2017). Many of the classes bear strong similarity to each other, most noticeably the first three classes all appear strongly conserved across all taxa, much like the first class of the 3-class model.…”
Section: Resultsmentioning
confidence: 99%
“…We used IQ-TREE (Nguyen et al, 2015) to fit a GHOST model to the sequence alignment. We used the model selection procedure outlined in Crotty et al (2017) to choose the model of sequence evolution and number of classes. After performing the inference we analysed the site-wise probabilities of evolving under each inferred class to identify influential sites, contiguous regions and genes within the genome.…”
Section: Methodsmentioning
confidence: 99%
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