A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios

Abstract

The reconstruction of ancestral scenarios is widely used to study the evolution of characters along phylogenetic trees. One commonly uses the marginal posterior probabilities of the character states, or the joint reconstruction of the most likely scenario. However, marginal reconstructions provide users with state probabilities, which are difficult to interpret and visualize, whereas joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences. We propose a simple and fast approach, which is in between these two extremes. We use decision-theory concepts (namely, the Brier score) to associate each node in the tree to a set of likely states. A unique state is predicted in tree regions with low uncertainty, whereas several states are predicted in uncertain regions, typically around the tree root. To visualize the results, we cluster the neighboring nodes associated with the same states and use graph visualization tools. The method is implemented in the PastML program and web server. The results on simulated data demonstrate the accuracy and robustness of the approach. PastML was applied to the phylogeography of Dengue serotype 2 (DENV2), and the evolution of drug resistances in a large HIV data set. These analyses took a few minutes and provided convincing results. PastML retrieved the main transmission routes of human DENV2 and showed the uncertainty of the human-sylvatic DENV2 geographic origin. With HIV, the results show that resistance mutations mostly emerge independently under treatment pressure, but resistance clusters are found, corresponding to transmissions among untreated patients.

Phylogeography of Puumala orthohantavirus in Europe

“…To perform the ancestral character state reconstructions by ML we used two recently published programs: PastView [56,57] and PastML [58,59]. Both methods are based on a F81-like marginal posteriors inference [60] with an optimized scaling factor.…”

“…Analyses were performed on the rooted phylogenetic tree previously computed by PhyML, with annotated tips (geographical origin of each isolate). The specificity of the PastML method is the use of a decision-theory concept and a Brier criterion to predict a unique state if the node is associated with low uncertainty, or several state if this uncertainty is high [59]. Reconstructions were performed as recommended by the authors (marginal posterior probabilities approximation (MPPA) under a F81-like model).…”

nosoi: A stochastic agent‐based transmission chain simulation framework in r

“…As such, it enables accurate testing of popular inference methods in both discrete and continuous phylogeography using either maximum-likelihood (Ishikawa, Zhukova, Iwasaki, & Gascuel, 2019) or Bayesian inference (Lemey, Rambaut, Drummond, & Suchard, 2009;Lemey et al, 2010;Suchard et al, 2018), which are widely used in pathogen phylodynamics. In that regard, an interesting application of our proposed simulation framework could be to study the increasingly popular structured coalescent models nosoi enables the simulation of real-life scenarios of viral outbreaks, and we provide several example scenarios to showcase its capabilities to generate a single transmission chain using different settings.…”

Holocentric Karyotype Evolution in Rhynchospora Is Marked by Intense Numerical, Structural, and Genome Size Changes

“…The best fitting model was assessed using the AIC (Glick and Mayrose, 2014). Furthermore, the ancestral states of chromosome number along the branches were estimated using PastML (Ishikawa et al, 2019) applying two prediction methods, Maximum Likelihood (JOINT+F81) and Maximum Parsimony (Accelerated Transformation), respectively. Since these analyses can take only one state per sample, only the lowest chromosome number for each species was used in the case of samples with more than one cytotype, with different chromosome numbers and ploidy levels.…”

“…number reconstruction based on the phylogenetic tree with PastML ( Figure 7) and ChromEvol (Supplementary data 12). Based on the ancestral character reconstruction with PastML (Ishikawa et al, 2019) karyotypes with n = 5 or 10 were the most likely ancestral complement, based on maximum likelihood (ML) and maximum parsimony (MP), respectively (Figure 7). The ChromEvol model reported an ACN of n = 5 and variations were mostly attributed to polyploidization (1.39), chromosome fusion (1.13) and less frequently to fission (0.76) (Supplementary data 12 shows much more support for n = 5), according to the optimal model selected by means of the Akaike information Criterion (AIC) (Akaike, 1974).…”