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Motivation Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic analyses, having accurate, fast, and interpretable scores is of high importance. Results Here, we employed a data-driven approach to estimate branch support values with a probabilistic interpretation. To this end, we simulated thousands of realistic phylogenetic trees and the corresponding multiple sequence alignments. Each of the obtained alignments was used to infer the phylogeny using state-of-the-art phylogenetic inference software, which was then compared to the true tree. Using these extensive data, we trained machine-learning algorithms to estimate branch support values for each bipartition within the maximum-likelihood trees obtained by each software. Our results demonstrate that our model provides fast and more accurate probability-based branch support values than commonly used procedures. We demonstrate the applicability of our approach on empirical datasets. Availability and implementation The data supporting this work are available in the Figshare repository at https://doi.org/10.6084/m9.figshare.25050554.v1 , and the underlying code is accessible via GitHub at https://github.com/noaeker/bootstrap_repo. [ABSTRACT FROM AUTHOR]

The computational search for the maximum-likelihood phylogenetic tree is an NP-hard problem. As such, current tree search algorithms might result in a tree that is the local optima, not the global one. Here, we introduce a paradigm shift for predicting the maximum-likelihood tree, by approximating long-term gains of likelihood rather than maximizing likelihood gain at each step of the search. Our proposed approach harnesses the power of reinforcement learning to learn an optimal search strategy, aiming at the global optimum of the search space. We show that when analyzing empirical data containing dozens of sequences, the log-likelihood improvement from the starting tree obtained by the reinforcement learning–based agent was 0.969 or higher compared to that achieved by current state-of-the-art techniques. Notably, this performance is attained without the need to perform costly likelihood optimizations apart from the training process, thus potentially allowing for an exponential increase in runtime. We exemplify this for data sets containing 15 sequences of length 18,000 bp and demonstrate that the reinforcement learning–based method is roughly three times faster than the state-of-the-art software. This study illustrates the potential of reinforcement learning in addressing the challenges of phylogenetic tree reconstruction. [ABSTRACT FROM AUTHOR]

Premise of research. Polyploidy, a major evolutionary process in flowering plants, is expected to impact floral traits, which can have cascading effects on pollination interactions, but this may depend on selfing propensity. In a novel use of herbarium specimens, we assessed the effects of polyploidy and mating system on floral traits and the pollination niche of 40 Brassicaceae species. Methodology. We combined data on mating system (self-compatible or self-incompatible) with inferred ploidy level (polyploid or diploid) and used phylogenetically controlled analyses to investigate their influence on floral traits (size and shape) and the degree of pollination generalism based on the frequency and the richness of heterospecific pollen morphospecies captured by stigmas. Pivotal results. Flower size (but not shape) depended on the interaction between ploidy and mating system. Self-incompatible polyploid species had larger flowers than self-incompatible diploids, but there was no difference for self-compatible species. The breadth of pollination niche (degree of generalism) was not affected by ploidy but was rather strongly affected by mating system only. Self-incompatible species had more stigmas with heterospecific pollen and higher heterospecific pollen morphospecies richness per stigma than self-compatible species, regardless of their ploidy. Conclusions. Our results demonstrate that mating system moderated the influence of ploidy on morphological features associated with pollination generalism but that response in terms of heterospecific pollen captured as a proxy of pollination generalism was more variable. [ABSTRACT FROM AUTHOR]

Motivation In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale alignments are challenging for likelihood-based phylogenetic inference programs and usually require using a powerful computer cluster. Current tools for alignment trimming prior to phylogenetic analysis do not promise a significant reduction in the alignment size and are claimed to have a negative effect on the accuracy of the obtained tree. Results Here, we propose an artificial-intelligence-based approach, which provides means to select the optimal subset of sites and a formula by which one can compute the log-likelihood of the entire data based on this subset. Our approach is based on training a regularized Lasso-regression model that optimizes the log-likelihood prediction accuracy while putting a constraint on the number of sites used for the approximation. We show that computing the likelihood based on 5% of the sites already provides accurate approximation of the tree likelihood based on the entire data. Furthermore, we show that using this Lasso-based approximation during a tree search decreased running-time substantially while retaining the same tree-search performance. Availability and implementation The code was implemented in Python version 3.8 and is available through GitHub (https://github.com/noaeker/lasso%5fpositions%5fsampling). The datasets used in this paper were retrieved from Zhou et al. (2018) as described in section 3. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]