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Introns are highly prevalent in most eukaryotic genomes. Despite the accumulating evidence for benefits conferred by the possession of introns, their specific roles and functions, as well as the processes shaping their evolution, are still only partially understood. Here, we explore the evolution of the eukaryotic intron–exon gene structure by focusing on several key features such as the intron length, the number of introns, and the intron-to-exon length ratio in protein-coding genes. We utilize whole-genome data from 590 species covering the main eukaryotic taxonomic groups and analyze them within a statistical phylogenetic framework. We found that the basic gene structure differs markedly among the main eukaryotic groups, with animals, and particularly chordates, displaying intron-rich genes, compared with plants and fungi. Reconstruction of gene structure evolution suggests that these differences evolved prior to the divergence of the main phyla and have remained mostly conserved within groups. We revisit the previously reported association between the genome size and the mean intron length and report that this association differs considerably among phyla. Analyzing a large and diverse dataset of species with whole-genome information while applying advanced modeling techniques allowed us to obtain a global evolutionary perspective. Our findings may indicate that introns play different molecular and evolutionary roles in different organisms. [ABSTRACT FROM AUTHOR]

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]