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The Andes mountains of western South America are a globally important biodiversity hotspot, yet there is a paucity of resolved phylogenies for plant clades from this region. Filling an important gap in our understanding of the World's richest flora, we present the first phylogeny of Freziera (Pentaphylacaceae), an Andean-centered, cloud forest radiation. Our dataset was obtained via hybrid-enriched target sequence capture of Angiosperms353 universal loci for 50 of the ca. 75 spp. obtained almost entirely from herbarium specimens. We identify high phylogenomic complexity in Freziera , including the presence of data artifacts. Via by-eye observation of gene trees, detailed examination of warnings from recently improved assembly pipelines, and gene tree filtering, we identified that artifactual orthologs (i.e. the presence of only one copy of a multicopy gene due to differential assembly) were an important source of gene tree heterogeneity that had a negative impact on phylogenetic inference and support. These artifactual orthologs may be common in plant phylogenomic datasets, where multiple instances of genome duplication are common. After accounting for artifactual orthologs as source of gene tree error, we identified a significant, but nonspecific signal of introgression using Patterson's D and f4 statistics. Despite phylogenomic complexity, we were able to resolve Freziera into 9 well-supported subclades whose evolution has been shaped by multiple evolutionary processes, including incomplete lineage sorting, historical gene flow, and gene duplication. Our results highlight the complexities of plant phylogenomics, which are heightened in Andean radiations, and show the impact of filtering data processing artifacts and standard filtering approaches on phylogenetic inference. [ABSTRACT FROM AUTHOR]

Understanding the evolution of biodiversity on Earth is a central aim in biology. Currently, various disciplines of science contribute to unravel evolution at all levels of life, from individual organisms to species and higher ranks, using different approaches and specific terminologies. The search for common origin, traditionally called homology, is a connecting paradigm of all studies related to evolution. However, it is not always sufficiently taken into account that defining homology depends on the hierarchical level studied (organism, population, and species), which can cause confusion. Therefore, we propose a framework to define homologies making use of existing terms, which refer to homology in different fields, but restricting them to an unambiguous meaning and a particular hierarchical level. We propose to use the overarching term "homology" only when "morphological homology," "vertical gene transfer," and "phylogenetic homology" are confirmed. Consequently, neither phylogenetic nor morphological homology is equal to homology. This article is intended for readers with different research backgrounds. We challenge their traditional approaches, inviting them to consider the proposed framework and offering them a new perspective for their own research. [ABSTRACT FROM AUTHOR]

Several well-documented evolutionary processes are known to cause conflict between species-level phylogenies and gene-level phylogenies. Three of the most challenging processes for species tree inference are incomplete lineage sorting, hybridization and gene duplication, which may result in unwarranted comparisons of paralogous genes. Several existing methods have dealt with these processes but none has yet been able to untangle all three at once. Here,we propose a stepwise method by which these processes can be discerned using information on genomic location coupled with coalescent simulations. In the first step, highly discordant genes within genomic blocks (putative paralogs) are identified and excluded from the data set and, in the second step, blocks of linked genes are grouped according to their hybrid history. Existing multispecies coalescent software can then be applied to recover the principal tree(s) that make up the species tree/network without violating the underlying model. The potential of the approach is evaluated on simulated data derived from a species network composed of nine species, of which one is of hybrid origin, and displaying a single-gene duplication that leads to paralogous comparisons.We apply our method to an empirical set of 12 genes from 7 species sampled in the plant genus Medicago that display phylogenetic discordance.We identify the causes of the discordance and demonstrate that the Medicago orbicularis lineage experienced an episode of ancient hybridization. Our results show promise as a new way to explore phylogenetic sequence data that can significantly improve species tree inference in presence of hybridization and undetected paralogy or other causes leading to extremely discordant gene trees. [ABSTRACT FROM AUTHOR]

Orthology analysis, that is, finding out whether a pair of homologous genes are orthologs -- stemming from a speciation--or paralogs--stemming from a gene duplication - is of central importance in computational biology, genome annotation, and phylogenetic inference. In particular, an orthologous relationship makes functional equivalence of the two genes highly likely. A major approach to orthology analysis is to reconcile a gene tree to the corresponding species tree, (most commonly performed using the most parsimonious reconciliation, MPR). However, most such phylogenetic orthology methods infer the gene tree without considering the constraints implied by the species tree and, perhaps even more importantly, only allow the gene sequences to influence the orthology analysis through the a priori reconstructed gene tree.We propose a sound, comprehensive Bayesian Markov chain Monte Carlo-based method, DLRSOrthology, to compute orthology probabilities. It efficiently sums over the possible gene trees and jointly takes into account the current gene tree, all possible reconciliations to the species tree, and the, typically strong, signal conveyed by the sequences. We compare our method with PrIME-GEM, a probabilistic orthology approach built on a probabilistic duplication-loss model, and MRBAYESMPR, a probabilistic orthology approach that is based on conventional Bayesian inference coupled with MPR.We find that DLRSOrthology outperforms these competing approaches on synthetic data as well as on biological data sets and is robust to incomplete taxon sampling artifacts. [ABSTRACT FROM AUTHOR]

Orthology analysis aims at identifying orthologous genes and gene products from different organisms and, therefore, is a powerful tool in modern computational and experimental biology. Although reconciliation-based orthology methods are generally considered more accurate than distance-based ones, the traditional parsimony-based implementation of reconciliation-based orthology analysis (most parsimonious reconciliation [MPR]) suffers from a number of shortcomings. For example, 1) it is limited to orthology predictions from the reconciliation that minimizes the number of gene duplication and loss events, 2) it cannot evaluate the support of this reconciliation in relation to the other reconciliations, and 3) it cannot make use of prior knowledge (e.g., about species divergence times) that provides auxiliary information for orthology predictions. We present a probabilistic approach to reconciliation-based orthology analysis that addresses all these issues by estimating orthology probabilities. The method is based on the gene evolution model, an explicit evolutionary model for gene duplication and gene loss inside a species tree, that generalizes the standard birth–death process. We describe the probabilistic approach to orthology analysis using 2 experimental data sets and show that the use of orthology probabilities allows a more informative analysis than MPR and, in particular, that it is less sensitive to taxon sampling problems. We generalize these anecdotal observations and show, using data generated under biologically realistic conditions, that MPR give false orthology predictions at a substantial frequency. Last, we provide a new orthology prediction method that allows an orthology and paralogy classification with any chosen sensitivity/specificity combination from the spectra of achievable combinations. We conclude that probabilistic orthology analysis is a strong and more advanced alternative to traditional orthology analysis and that it provides a framework for sophisticated comparative studies of processes in genome evolution. [ABSTRACT FROM PUBLISHER]