Bui Quang Minh

IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3–97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.

Ultrafast Approximation for Phylogenetic Bootstrap

Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira–Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66–33.3) to 10.2 (range: 1.32–41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.

ModelFinder: fast model selection for accurate phylogenetic estimates

Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.

W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis

This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, binary and morphology) in common alignment formats and a wide range of evolutionary models including mixture and partition models. W-IQ-TREE performs fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests. All computations are conducted on a dedicated computer cluster and the users receive the results via URL or email. W-IQ-TREE is available at http://iqtree.cibiv.univie.ac.at. It is free and open to all users and there is no login requirement.

Terrace Aware Data Structure for Phylogenomic Inference from Supermatrices

In phylogenomics the analysis of concatenated gene alignments, the so-called supermatrix, is commonly accompanied by the assumption of partition models. Under such models each gene, or more generally partition, is allowed to evolve under its own evolutionary model. Although partition models provide a more comprehensive analysis of supermatrices, missing data may hamper the tree search algorithms due to the existence of phylogenetic (partial) terraces. Here, we introduce the phylogenetic terrace aware (PTA) data structure for the efficient analysis under partition models. In the presence of missing data PTA exploits (partial) terraces and induced partition trees to save computation time. We show that an implementation of PTA in IQ-TREE leads to a substantial speedup of up to 4.5 and 8 times compared with the standard IQ-TREE and RAxML implementations, respectively. PTA is generally applicable to all types of partition models and common topological rearrangements thus can be employed by all phylogenomic inference software.

Distribution and Phylogeny of Light-Oxygen-Voltage-Blue-Light-Signaling Proteins in the Three Kingdoms of Life†

Plants and fungi respond to environmental light stimuli via the action of different photoreceptor modules. One such class, responding to the blue region of light, is constituted by photoreceptors containing so-called light-oxygen-voltage (LOV) domains as sensor modules. Four major LOV families are currently identified in eukaryotes: (i) the plant phototropins, regulating various physiological effects such as phototropism, chloroplast relocation, and stomatal opening; (ii) the aureochromes, mediating photomorphogenesis in photosynthetic stramenopile algae; (iii) the plant circadian photoreceptors of the zeitlupe (ZTL)/adagio (ADO)/flavin-binding Kelch repeat F-box protein 1 (FKF1) family; and (iv) the fungal circadian photoreceptors white-collar 1 (WC-1). Blue-light-sensitive LOV signaling modules are also widespread throughout the prokaryotic world, and physiological responses mediated by bacterial LOV photoreceptors were recently reported. Thus, the question arises as to the evolutionary relationship between the pro- and eukaryotic LOV photoreceptor systems. We used Bayesian and maximum-likelihood tree reconstruction methods to infer evolutionary scenarios that might have led to the widespread appearance of LOV domains among the pro- and eukaryotes. The phylogenetic study presented here suggests a bacterial origin for the LOV domains of the four major eukaryotic LOV photoreceptor families, whereas the LOV sensor domains were most likely recruited from the bacteria in the course of plastid and mitochondrial endosymbiosis.

Decisive Data Sets in Phylogenomics: Lessons from Studies on the Phylogenetic Relationships of Primarily Wingless Insects

Phylogenetic relationships of the primarily wingless insects are still considered unresolved. Even the most comprehensive phylogenomic studies that addressed this question did not yield congruent results. To get a grip on these problems, we here analyzed the sources of incongruence in these phylogenomic studies by using an extended transcriptome data set. Our analyses showed that unevenly distributed missing data can be severely misleading by inflating node support despite the absence of phylogenetic signal. In consequence, only decisive data sets should be used which exclusively comprise data blocks containing all taxa whose relationships are addressed. Additionally, we used Four-cluster Likelihood Mapping (FcLM) to measure the degree of congruence among genes of a data set, as a measure of support alternative to bootstrap. FcLM showed incongruent signal among genes, which in our case is correlated neither with functional class assignment of these genes nor with model misspecification due to unpartitioned analyses. The herein analyzed data set is the currently largest data set covering primarily wingless insects, but failed to elucidate their interordinal phylogenetic relationships. Although this is unsatisfying from a phylogenetic perspective, we try to show that the analyses of structure and signal within phylogenomic data can protect us from biased phylogenetic inferences due to analytical artifacts.

UFBoot2: Improving the Ultrafast Bootstrap Approximation

&NA; 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. Here, 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 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ‐TREE software package version 1.6 and freely available at http://www.iqtree.org.

Phylogenetic Diversity within Seconds

We consider a (phylogenetic) tree with n labeled leaves, the taxa, and a length for each branch in the tree. For any subset of k taxa, the phylogenetic diversity is defined as the sum of the branch-lengths of the minimal subtree connecting the taxa in the subset. We introduce two time-efficient algorithms (greedy and pruning) to compute a subset of size k with maximal phylogenetic diversity in O(n log k) and O[n + (n-k) log (n-k)] time, respectively. The greedy algorithm is an efficient implementation of the so-called greedy strategy (Steel, 2005; Pardi and Goldman, 2005), whereas the pruning algorithm provides an alternative description of the same problem. Both algorithms compute within seconds a subtree with maximal phylogenetic diversity for trees with 100,000 taxa or more.

Discovery of the first light-dependent protochlorophyllide oxidoreductase in anoxygenic phototrophic bacteria

In all photosynthetic organisms, chlorophylls function as light‐absorbing photopigments allowing the efficient harvesting of light energy. Chlorophyll biosynthesis recurs in similar ways in anoxygenic phototrophic proteobacteria as well as oxygenic phototrophic cyanobacteria and plants. Here, the biocatalytic conversion of protochlorophyllide to chlorophyllide is catalysed by evolutionary and structurally distinct protochlorophyllide reductases (PORs) in anoxygenic and oxygenic phototrophs. It is commonly assumed that anoxygenic phototrophs only contain oxygen‐sensitive dark‐operative PORs (DPORs), which catalyse protochlorophyllide reduction independent of the presence of light. In contrast, oxygenic phototrophs additionally (or exclusively) possess oxygen‐insensitive but light‐dependent PORs (LPORs). Based on this observation it was suggested that light‐dependent protochlorophyllide reduction first emerged as a consequence of increased atmospheric oxygen levels caused by oxygenic photosynthesis in cyanobacteria. Here, we provide experimental evidence for the presence of an LPOR in the anoxygenic phototrophic α‐proteobacterium Dinoroseobacter shibae DFL12T. In vitro and in vivo functional assays unequivocally prove light‐dependent protochlorophyllide reduction by this enzyme and reveal that LPORs are not restricted to cyanobacteria and plants. Sequence‐based phylogenetic analyses reconcile our findings with current hypotheses about the evolution of LPORs by suggesting that the light‐dependent enzyme of D. shibae DFL12T might have been obtained from cyanobacteria by horizontal gene transfer.