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bioRxiv2019

Lexical landscapes as large in silico data forexamining advanced properties of fitness landscapes

In silico approaches have served a central role in the development of evolutionary theory for generations. This especially applies to the concept of the fitness landscape, one of the most important abstractions in evolutionary genetics, and one which has benefited from the presence of large empirical data sets only in the last decade or so. In this study, we propose a method that allows us to generate enormous data sets that walk the line between in silico and empirical: word usage frequencies as catalogued by the Google ngram corpora. These data can be codified or analogized in terms of a multidimensional empirical fitness landscape towards the examination of advanced concepts--adaptive landscape by environment interactions, clonal competition, higher-order epistasis and countless others. We argue that the greater Lexical Landscapes approach can serve as a platform that offers an astronomical number of fitness landscapes for exploration (at least) or theoretical formalism (potentially) in evolutionary biology.

evolutionary biology↗
bioRxiv2019

AEGIS: An In Silico Tool to Model Genome Evolution in Age-Structured Populations

AEGIS (Ageing of Evolving Genomes In Silico) is a versatile population-genetics numerical-simulation tool that enables the evolution of life history trajectories under sexual and asexual reproduction and a wide variety of evolutionary constraints. By encoding age-specific survival and reproduction probabilities as discrete genomic elements, AEGIS allows these probabilities to evolve freely and independently over time. Simulation of population evolution with AEGIS demonstrates that ageing-like phenotypes evolve in stable environments under a wide range of conditions, that life history trajectories depend heavily on mutation rates, and that sexual populations are better able to accumulate high levels of beneficial mutations affecting early-life survival and reproduction. AEGIS is free and open-source, and aims to become a standard reference tool in the study of life-history evolution and the evolutionary biology of ageing.

evolutionary biology↗
bioRxiv2026

Adaptive laboratory evolution of a minimal cell to low temperature

Temperature is a fundamental constraint on life. Most organisms adapt to temperature change through complex regulatory and stress-response systems. Whether such adaptation is possible in a genomically minimal cell remains unclear. Here, adaptive laboratory evolution was performed on the minimal genome bacterium JCVI-syn3B, shifting growth from 37{degrees}C to 25{degrees}C, a temperature at which the original strain cannot grow. After 40 serial passages, the evolved strains exhibited robust growth at 25{degrees}C. Multi-omics analyses revealed the adaptation mechanisms including increased mRNA turnover, enhanced DNA unwinding and stabilization of replication intermediates, elevated glycerolipid synthesis, and upregulation of division proteins associated with Z-ring assembly. Whole-genome transplantation was performed to distinguish genetic or non-genetic contributions, demonstrating that the cold-adapted phenotype was largely genetically encoded. These results indicated that even a minimal cell retains substantial evolvability and highlight its potential as a tractable research platform for cellular and evolutionary biology.

evolutionary biology↗
bioRxiv2021

Phylogenomic Analyses Of 2,786 Genes In 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts (Animals, Fungi and Their Microbial Relatives) and All Other Lineages

Advances in phylogenetics and high-throughput sequencing have allowed the reconstruction of deep phylogenetic relationships in the evolution of eukaryotes. Yet, the root of the eukaryotic tree of life remains elusive. The most popular hypothesis in textbooks and reviews is a root between Unikonta (Opisthokonta + Amoebozoa) and Bikonta (all other eukaryotes), which emerged from analyses of a single gene fusion. Subsequent highly-cited studies based on concatenation of genes supported this hypothesis with some variations or proposed a root within Excavata. However, concatenation of genes neither considers phylogenetically-informative events (i.e. gene duplications and losses), nor provides an estimate of the root. A more recent study using gene tree-species tree reconciliation methods suggested the root lies between Opisthokonta and all other eukaryotes, but only including 59 taxa and 20 genes. Here we apply a gene tree - species tree reconciliation approach to a gene-rich and taxon-rich dataset (i.e. 2,786 gene families from two sets of ~158 diverse eukaryotic lineages) to assess the root, and we iterate each analysis 100 times to quantify tree space uncertainty. We estimate a root between Fungi and all other eukaryotes, or between Opisthokonta and all other eukaryotes, and reject alternative roots from the literature. Based on further analysis of genome size we propose Opisthokonta + others as the most likely root. Impact summaryFinding the root of the eukaryotic tree of life is critical for the field of comparative biology as it allows us to understand the timing and mode of evolution of characters across the evolutionary history of eukaryotes. But this is one of the most challenging questions in evolutionary biology because the age (~1.8 billion years), diversity and complexity of eukaryotes challenge phylogenomic methods. This study evaluates the root using reconciliation of gene trees and species trees instead of the more common approach of analyzing concatenated genes. The dataset used in this study is bigger and more taxonomically inclusive than the dataset of any previous study about the eukaryotic root, and supports a root at or within Opisthokonta (e.g. animals and fungi). Finally, we explicitly test alternative hypotheses from the literature, and again find support for an Opisthokonta root.

evolutionary biology↗
bioRxiv2020

Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system

Delimiting species boundaries is a major goal in evolutionary biology. An increasing body of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and demographic fluctuations. New methods based on model selection, such as approximate Bayesian computation, approximate likelihoods, and machine learning are promising tools arising in this field. Here, we introduce a framework for species delimitation using the multispecies coalescent model coupled with a deep learning algorithm based on convolutional neural networks (CNNs). We compared this strategy with a similar ABC approach. We applied both methods to test species boundary hypotheses based on current and previous taxonomic delimitations as well as genetic data (sequences from 41 loci) in Pilosocereus aurisetus, a cactus species complex with a sky-island distribution and taxonomic uncertainty. To validate our method, we also applied the same strategy on data from widely accepted species from the genus Drosophila. The results show that our CNN approach has high capacity to distinguish among the simulated species delimitation scenarios, with higher accuracy than ABC. For the cactus dataset, a splitter hypothesis without gene flow showed the highest probability in both CNN and ABC approaches, a result agreeing with previous taxonomic classifications and in line with the sky-island distribution and low dispersal features of P. aurisetus. Our results highlight the cryptic diversity within the P. aurisetus complex and show that CNNs are a promising approach for distinguishing complex evolutionary histories, even outperforming the accuracy of other model-based approaches such as ABC.

evolutionary biology↗
bioRxiv2025

Developmental bias explains the evolutionary trend towards simple leaf shapes

The relative influence of developmental bias and natural selection on evolutionary outcomes remains a central, unresolved question in evolutionary biology. Here, we combine large-scale phylogenetic trees with herbarium data to confirm that, across the evolutionary history of angiosperms, transitions from complex to simple leaf shapes have occurred at a significantly higher rate than the reverse. To investigate whether this directional trend reflects an underlying developmental bias, we simulate evolutionary transitions by introducing random mutations into a well-established computational model of leaf development. Despite excluding selection, the transition rates produced by the model closely align with those observed in the phylogenetic data. This agreement indicates that a strong developmental bias, reflecting the relative ease with which gene regulatory networks generate simple over complex leaves, has played a major role in shaping the evolution of leaf morphology. Our findings echo recent work showing that similar biases towards simplicity influence the evolution of molecular structures, including RNA secondary structures and protein quaternary assemblies. Together, these results suggest that a developmental bias favouring simplicity may be a widespread and general feature of evolutionary systems.

evolutionary biology↗
bioRxiv2026

The Speciation Continuum in Bloom: Incomplete Lineage Sorting, Gene Flow, and Reticulate Evolution in Rapidly Diverging Plant Lineages

The remarkable diversity of life on Earth results from evolutionary processes functioning across different spatial and temporal scales. Species diversification occurs through various mechanisms and at widely varying rates, but identifying the conditions that trigger bursts of diversification over short timescales remains a central challenge in evolutionary biology. This difficulty is more pronounced when incomplete lineage sorting (ILS), hybridization, and ongoing gene flow obscure evolutionary relationships and complicate species delimitation. In this study, we investigated the evolutionary history and species boundaries within a group of recently diverged Petunia lineages shaped by pervasive gene flow. We integrated phylogenomic, population genetic, and species delimitation approaches to reconstruct lineage relationships and assess whether these lineages represent distinct species or stages along a speciation continuum. By applying methods that account for both ILS and gene flow, we revealed that most lineages are not fully independent evolutionary units but rather occupy intermediate positions along this continuum. Gene flow played a crucial role during diversification, blurring species boundaries and generating reticulate evolutionary patterns. Our findings demonstrate that traditional phylogenetic trees may oversimplify relationships in such systems, while phylogenetic networks offer a more accurate representation of evolutionary history. Comprehensive and integrative analyses, such as those employed here, are essential for capturing these complex dynamics. Ultimately, only four lineages could be confidently recognized as distinct species, whereas the remaining represent cases of ongoing divergence. These results emphasize the need to refine species delimitation frameworks for systems characterized by recent divergence and extensive reticulation.

evolutionary biology↗
bioRxiv2020

An optimal lysis time maximizes bacteriophage fitness in quasi-continuous culture

Optimality models have a checkered history in evolutionary biology. While optimality models have been successful in providing valuable insight into the evolution of a wide variety of biological traits, a common objection is that optimality models are overly simplistic and ignore organismal genetics. We revisit evolutionary optimization in the context of a major bacteriophage life history trait, lysis time. Lysis time refers to the period spanning phage infection of a host cell and its lysis, whereupon phage progeny are released. Lysis time, therefore, directly determines phage fecundity assuming progeny assembly rate is maximized. Noting that previous tests of lysis time optimality rely on batch culture, we implemented a quasi-continuous culture system to observe productivity of a panel of isogenic phage {lambda} mutants differing in lysis time. We report that {lambda} phage productivity in our experiments is maximized around an optimal lysis time of 63 min, which is the lysis time of the {lambda} "wildtype" strain. We discuss this finding in light of recent results that lysis time variation is also minimized in the {lambda} "wildtype" strain.

evolutionary biology↗
bioRxiv2024

Rapid speciation in the holopelagic ctenophore Mnemiopsis following glacial recession

BackgroundUnderstanding how populations diverge is one of the most compelling questions in evolutionary biology but our grasp on the genomic mechanisms underpinning divergence is limited to a handful of species. Indeed, we know even less about divergence in the pelagic zone, where barriers to gene flow are seemingly absent. The holopelagic ctenophore Mnemiopsis leidyi is the most widely used ctenophore in experimental biology and has become an important model system in studies ranging from developmental biology to neurobiology. In addition, its relatively small and tractable genome provides a powerful foundation for genomic and evolutionary analyses. However, we still lack a clear understanding of species boundaries, population structure, and the evolutionary forces shaping divergence within Mnemiopsis, limiting both evolutionary and ecological interpretations. To expand our general understanding of divergence across novel environments as well as resolve a long-standing taxonomic debate, we generated the most comprehensive genomic study to date of the holopelagic ctenophore Mnemiopsis across a large expanse of its native range. ResultsBy leveraging multiple analytical approaches and generating two near-chromosome level genomes, we identify two distinct species of Mnemiopsis with high levels of genome-wide divergence along the US Atlantic coast, which correspond to M. leidyi and M. gardeni. Our demographic analyses suggest that M. leidyi and M. gardeni began to diverge during the mid-to-late Pleistocene climate transitions and were later shaped by post-glacial oceanographic changes. We highlight substantial genomic rearrangements and copy number variation between species as well as uncover key genes under selection that are likely important for environmental adaptation. ConclusionsTogether, these findings provide compelling evidence that the ctenophore currently recognized as M. leidyi represents more than one species. Recognizing cryptic species boundaries is critical for future study designs, environmental monitoring, and developing targeted management strategies. Altogether, we connect microevolutionary processes with macroevolutionary patterns and provide new insights into how ocean dynamics drive speciation and adaptation in pelagic ecosystems.

evolutionary biology↗
bioRxiv2018

A Simulation-Based Evaluation of Total-Evidence Dating Under the Fossilized Birth-Death Process

Bayesian molecular dating is widely used to study evolutionary timescales. This procedure usually involves phylogenetic analysis of nucleotide sequence data, with fossil-based calibrations applied as age constraints on internal nodes of the tree. An alternative approach is Bayesian total-evidence dating, which involves the joint analysis of molecular data from present-day taxa and morphological data from both extant and fossil taxa. Part of its appeal stems from the fossilized birth-death process, which provides a model of lineage diversification for the prior on the tree topology and node times. However, total-evidence dating faces a number of considerable challenges, especially those associated with fossil sampling and evolutionary models for morphological characters. We conducted a simulation study to evaluate the performance of total-evidence dating with the fossilized birth-death model. We simulated fossil occurrences and the evolution of nucleotide sequences and morphological characters under a wide range of conditions. Our analyses show that fossil occurrences have a greater influence than the degree of among-lineage rate variation or the number of morphological characters on estimates of node times and the tree topology. Total-evidence dating generally performs well in recovering the relationships among extant taxa, but has difficulties in correctly placing fossil taxa in the tree and identifying the number of sampled ancestors. The method yields accurate estimates of the origin time of the fossilized birth-death process and the ages of the root and crown group, although the precision of these estimates varies with the probability of fossil occurrence. The exclusion of morphological characters results in a slight overestimation of node times, whereas the exclusion of nucleotide sequences has a negative impact on inference of the tree topology. Overall, our results provide a detailed view of the performance of total-evidence dating, which will inform further development of the method and its application to key questions in evolutionary biology.

evolutionary biology↗
bioRxiv2018

A Darwinian Uncertainty Principle

Reconstructing ancestral characters and traits along a phylogenetic tree is central to evolutionary biology. It is the key to understanding morphology changes among species, inferring ancestral biochemical properties of life, and recovering migration routes in phylogeography. The goal is twofold: to reconstruct the character state at the tree root (e.g. the region of origin of some species), and to understand the process of state changes along the tree (e.g. species flow between countries). Although each goal can be achieved with high accuracy individually, we use mathematics and simulations to demonstrate that it is generally impossible to accurately estimate both the root state and the rates of state changes along the tree branches from the observed data at the tips of the tree. This inherent Darwinian uncertainty principle concerning the simultaneous estimation of pattern and process governs ancestral reconstructions in biology. Increasing the number of tips improves the joint estimation accuracy for certain tree shapes that arise in evolutionary models, however, for other trees shapes it does not.

evolutionary biology↗
bioRxiv2020

Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis

We study the genotype-phenotype maps of 80 quantitative phenotypes in the model plant Arabidopsis thaliana, by representing the genotypes affecting each phenotype as a genotype network. In such a network, each vertex or node corresponds to an individuals genotype at all those genomic loci that affect a given phenotype. Two vertices are connected by an edge if the associated genotypes differ in exactly one nucleotide. The 80 genotype networks we analyze are based on data from genome-wide association studies of 199 A. thaliana accessions. They form connected graphs whose topography differs substantially among phenotypes. We focus our analysis on the incidence of epistasis (non-additive interactions among mutations) because a high incidence of epistasis can reduce the accessibility of evolutionary paths towards high or low phenotypic values. We find epistatic interactions in 67 phenotypes, and in 51 phenotypes every pairwise mutant interaction is epistatic. Moreover, we find phenotype-specific differences in the fraction of accessible mutational paths to maximum phenotypic values. However, even though epistasis affects the accessibility of maximum phenotypic values, the relationships between genotypic and phenotypic change of our analyzed phenotypes are sufficiently smooth that some evolutionary paths remain accessible for most phenotypes, even where epistasis is pervasive. The genotype network representation we use can complement existing approaches to understand the genetic architecture of polygenic traits in many different organisms. Author summaryHow genotypic change relates to phenotypic change is a fundamental problem in evolutionary biology. A smooth relationship between genotypic and phenotypic change facilitates phenotypic evolution, whereas a rugged one hampers it, because evolutionary change on a rugged landscape does not allow a monotonic increase or decrease in phenotypic value. We use data from multiple genome-wide association studies for 80 different phenotypes in the thale cress Arabidopsis thaliana to construct 80 genotype-phenotype maps for these phenotypes. We use a network representation for each map, where a vertex corresponds to a genotype, and where two vertices are connected if they differ in a single nucleotide. We study the incidence of non-additive interactions between mutations as a proxy for the ease to reach extreme phenotypic values of these maps. This incidence varies greatly among phenotypes, but even where it is highest, some monotonic pathways to high or low phenotypic values exist for the vast majority of phenotypes, such that the underlying phenotypes are accessible to adaptive evolution.

evolutionary biology↗
bioRxiv2022

The cost of information acquisition by natural selection

Natural selection enriches genotypes that are well-adapted to their environment. Over successive generations, these changes to the frequencies of types accumulate information about the selective conditions. Thus, we can think of selection as an algorithm by which populations acquire information about their environment. Kimura (1961) pointed out that every bit of information that the population gains this way comes with a minimum cost in terms of unrealized fitness (substitution load). Due to the gradual nature of selection and ongoing mismatch of types with the environment, a population that is still gaining information about the environment has lower mean fitness than a counter-factual population that already has this information. This has been an influential insight, but here we find that experimental evolution of Escherichia coli with mutations in a RNA polymerase gene (rpoB) violates Kimuras basic theory. To overcome the restrictive assumptions of Kimuras substitution load and develop a more robust measure for the cost of selection, we turn to ideas from computational learning theory. We reframe the learning problem faced by an evolving population as a population versus environment (PvE) game, which can be applied to settings beyond Kimuras theory - such as stochastic environments, frequency-dependent selection, and arbitrary environmental change. We show that the learning theoretic concept of regret measures relative lineage fitness and rigorously captures the efficiency of selection as a learning process. This lets us establish general bounds on the cost of information acquisition by natural selection. We empirically validate these bounds in our experimental system, showing that computational learning theory can account for the observations that violate Kimuras theory. Finally, we note that natural selection is a highly effective learning process in that selection is an asymptotically optimal algorithm for the problem faced by evolving populations, and no other algorithm can consistently outperform selection in general. Our results highlight the centrality of information to natural selection and the value of computational learning theory as a perspective on evolutionary biology.

evolutionary biology↗
bioRxiv2020

Heterogeneous genetic basis of age at maturity in salmonid fishes

Understanding the genetic basis of repeated evolution of the same phenotype across taxa is a fundamental aim in evolutionary biology and has applications to conservation and management. However, the extent to which interspecific life-history trait polymorphisms share evolutionary pathways remains under-explored. We address this gap by studying the genetic basis of a key life-history trait, age at maturity, in four species of Pacific salmon (genus Oncorhynchus) that exhibit intra- and interspecific variation in this trait - Chinook Salmon, Coho Salmon, Sockeye Salmon, and Steelhead Trout. We tested for associations in all four species between age at maturity and two genome regions, six6 and vgll3, that are strongly associated with the same trait in Atlantic Salmon (Salmo salar). We also conducted a genome-wide association analysis in Steelhead to assess whether additional regions were associated with this trait. We found the genetic basis of age at maturity to be heterogeneous across salmonid species. Significant associations between six6 and age at maturity were observed in two of the four species, Sockeye and Steelhead, with the association in Steelhead being particularly strong in both sexes (p = 4.46x10-9 after adjusting for genomic inflation). However, no significant associations were detected between age at maturity and the vgll3 genome region in any of the species, despite its strong association with the same trait in Atlantic Salmon. We discuss possible explanations for the heterogeneous nature of the genetic architecture of this key life-history trait, as well as the implications of our findings for conservation and management.

evolutionary biology↗
bioRxiv2023

Ecological predictors of organelle genome evolution: Phylogenetic correlations with taxonomically broad, sparse, unsystematized data

Comparative analysis of variables across phylogenetically linked observations can reveal mechanisms and insights in evolutionary biology. As the taxonomic breadth of the sample of interest increases, challenges of data sparsity, poor phylogenetic resolution, and complicated evolutionary dynamics emerge. Here, we investigate a cross-eukaryotic question where all these problems exist: which organismal ecology features are correlated with gene retention in mitochondrial and chloroplast DNA (organelle DNA or oDNA). Through a wide palette of synthetic control studies, we characterize the specificity and sensitivity of a collection of parametric and non-parametric phylogenetic comparative approaches to identify relationships in the face of such sparse and awkward datasets. We combine and curate ecological data coupled to oDNA genome information across eukaryotes, including a new semi-automated approach for gathering data on organismal traits from less systematized open-access resources including encyclopedia articles on species and taxa. Combining this unique dataset with our benchmarked comparative approaches, we confirm support for several known links between organismal ecology and organelle gene retention, identify several previously unidentified relationships constituting possible ecological contributors to oDNA genome evolution, and provide support for a recently hypothesized link between environmental demand and oDNA retention. We, with caution, discuss the implications of these findings for organelle evolution and of this pipeline for broad comparative analyses in other fields.

evolutionary biology↗
bioRxiv2018

When one phenotype is not enough - divergent evolutionary trajectories govern venom variation in a widespread rattlesnake species

Understanding the relationship between genome, phenotypic variation, and the ecological pressures that act to maintain that variation, represents a fundamental challenge in evolutionary biology. Functional polymorphisms typically segregate in spatially isolated populations [1, 2] and/or discrete ecological conditions [3-5], whereas dissecting the evolutionary processes involved in adaptive geographic variation across a continuous spatial distribution is much more challenging [6]. Additionally, pleiotropic interactions between genes and phenotype often complicate the identification of specific genotype-phenotype links [7-8], and thus of the selective pressures acting on them. Animal venoms are ideal systems to overcome these constraints: they are complex and variable, yet easily quantifiable molecular phenotypes with a clear function and a direct link to both genome and fitness [9]. Here, we use dense and widespread population-level sampling of the Mohave rattlesnake, Crotalus scutulatus, and show that genomic structural variation at multiple loci underlies extreme geographic variation in venom composition, which is maintained despite extensive gene flow. Unexpectedly, selection for diet does not explain venom variation, contrary to the dominant paradigm of venom evolution, and neither does neutral population structure caused by past vicariance. Instead, different toxin genes correlate with distinct environmental factors, suggesting that divergent selective pressures can act on individual loci independently of their genomic proximity or co-expression patterns. Local-scale spatial heterogeneity thus appears to maintain a remarkably ancient complex of molecular phenotypes, which have been retained in populations that diverged more than 1.5-2 MYA, representing an exceptional case of long-term structural polymorphism. These results emphasize how the interplay between genomic architecture and spatial heterogeneity in selective pressures may facilitate the retention of functional polymorphisms of an adaptive phenotype.

evolutionary biology↗
bioRxiv2019

Mosaic evolution, constraint, and constructional opportunity in great apes

AbstractA major goal in post-synthesis evolutionary biology has been to better understand how complex interactions between traits drive movement along and facilitate the formation of distinct evolutionary pathways. I present analyses of a character matrix sampled across the haplorrhine skeleton that revealed several modules of characters displaying distinct patterns in macroevolutionary disparity. Comparison of these patterns to those in neurological development showed that early ape evolution was characterized by an intense regime of evolutionary and developmental flexibility. Shifting and reduced constraint in apes was met with episodic bursts in phenotypic innovation that built a wide array of functional diversity over a foundation of shared developmental and anatomical structure. Shifts in modularity drove dramatic evolutionary changes across the ape body plan in two distinct ways: 1) an episode of relaxed integration early in hominoid evolution coincided with bursts in evolutionary rate across multiple character suites; 2) the formation of two new trait modules along the branch leading to chimps and humans preceded rapid and dramatic evolutionary shifts in the carpus and pelvis. Changes to the structure of evolutionary mosaicism may correspond to enhanced evolvability that has a preadaptive effect by catalyzing later episodes of dramatic morphological remodeling.

evolutionary biology↗
bioRxiv2025

Domain-combination based pan-genomic Tree of Life

Reconstructing the Tree of Life (ToL) remains a central challenge in evolutionary biology. While sequence-based methods have resolved much of lifes diversity, deep evolutionary relationships often remain elusive, particularly across the three superkingdoms of life. Here, we present a complementary approach using genome-wide occurrence of protein domain combinations as phylogenetic signal. We analyzed 5,343 complete proteomes spanning Bacteria, Archaea, and Eukaryota, and inferred a pan-genomic ToL from [~]77,000 domain combinations, including isolated domains and domain pairs. Our reconstruction recovers major clades and captures both vertical inheritance and signals from non-vertical processes such as lateral gene transfer, endosymbiosis, and genome reduction. Ancestral reconstructions reveal functional innovations that mark key evolutionary transitions--such as the emergence of vesicle trafficking in Eukaryota or methanogenesis in Archaea. We found that organisms with reduced genomes, like Chlamydiota, tend to be misplaced in domain combination-based phylogenies due to sparse combinatorial data. Our results demonstrate that domain combinations carry a strong, evolutionary signal that is both biologically and functionally informative. While not a substitute for sequence-based phylogenetics, domain combination-based trees offer an alternative perspective, capable of integrating both vertical and lateral processes into a unified evolutionary framework. Author SummaryTraditional evolutionary trees are typically built from sequences, but this approach struggles to resolve deep branches in the Tree of Life. Here, we use combinations of protein domains--the functional modules that make up proteins to resolve major relationships in the tree. By analyzing over 5,000 complete proteomes from Bacteria, Archaea, and Eukaryotes, we reconstruct a Tree of Life that reflects both vertical inheritance and key evolutionary processes like endosymbiosis and gene transfer. This domain-based approach not only recovers known relationships but also reveals how major biological innovations emerged. Our results show that domain combinations provide a powerful and interpretable signal for studying evolution at a genomic scale.

evolutionary biology↗