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Minh, B. Q.

Publications and source records attributed to Minh, B. Q..

3 recordsLinked to original sources

Complex models of sequence evolution require accurate estimators as exemplified with the invariable site plus Gamma model

The invariable site plus {Gamma} model is widely used to model rate heterogeneity among alignment sites in maximum likelihood and Bayesian phylogenetic analyses. The proof that the invariable site plus continuous {Gamma} model is identifiable (model parameters can be inferred correctly given enough data) has increased the creditability of its application to phylogeny reconstruction. However, most phylogenetic software implement the invariable site plus discrete {Gamma} model, whose identifiability is likely but unproven. How well the parameters of the invariable site plus discrete {Gamma} model are estimated is still disputed. Especially the correlation of the fraction of invariable sites with the fractions of sites with a slow evolutionary rate is discussed as being problematic. We show that optimization heuristics as implemented in frequently used phylogenetic software cannot always reliably estimate the shape parameter, the proportion of invariable sites and the tree length. Here, we propose an improved optimization heuristic that accurately estimates the three parameters. While research efforts mainly focus on tree search methods, our results signify the equal importance of verifying and developing effective estimation methods for complex models of sequence evolution.

evolutionary biology

GHOST: Recovering Historical Signal from Heterotachously-evolved Sequence Alignments

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 heterotachously-evolved sequences. We develop a model selection algorithm based on simulation results, and investigate the performance of the GHOST model on empirical data by sampling phylogenomic alignments of varying lengths from a plastome alignment. We then carry out inference under the GHOST model on a phylogenomic dataset composed of 248 genes from 16 taxa, where we find the GHOST model concurs with the currently accepted view, placing turtles as a sister lineage of archosaurs, in contrast to results obtained using traditional variable rates-across-sites models. Finally, we apply the model to a dataset composed of a sodium channel gene of 11 fish taxa, finding that the GHOST model is able to infer a subtle component of the historical signal, linked to the previously established convergent evolution of the electric organ in two geographically distinct lineages of electric fish. We compare inference under the GHOST model to partitioning by codon position and show that, owing to the minimization of model constraints, the GHOST model is able to offer unique biological insights when applied to empirical data.

evolutionary biology

UFBoot2: Improving the Ultrafast Bootstrap Approximation

The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches.\n\nHere, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 and 8.4 times (median) faster than SBS and RAxML rapid bootstrap on tested datasets, respectively. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.

evolutionary biology