Phylogenetic methods accurately reconstruct the universal tree of life
the verdict
CONTESTED PARTIAL
refutedsupported
the weight of evidence
10 sources for · 2 against
The evidence indicates that while phylogenetic methods successfully reconstruct specific trees and incorporate genomic data, constructing an accurate universal tree of life remains challenging and subject to debate due to factors such as horizontal gene transfer and model limitations.
Knowing phylogenetic relationships among species is fundamental for many studies in biology. An accurate phylogenetic tree underpins our understanding of the major transitions in evolution, such as the emergence of new body plans or metabolism, and is key to inferring the origin of new genes, detecting molecular adaptation, understanding morphological character evolution and reconstructing demographic changes in recently diverged species. Although data are ever more plentiful and powerful analysis methods are available, there remain many challenges to reliable tree building. Here, we discuss the major steps of phylogenetic analysis, including identification of orthologous genes or proteins, multiple sequence alignment, and choice of substitution models and inference methodologies. Understanding the different sources of errors and the strategies to mitigate them is essential for assembling an accurate tree of life.
Similar content being viewed by others PhyloTune: An efficient method to accelerate phylogenetic updates using a pretrained DNA language model Article Open access 26 July 2025 Incongruence in the phylogenomics era Article 27 June 2023 Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree Article Open access 20 April 2023 Explore related subjects Discover the latest articles and news in related subjects. Phylogenomics Molecular evolution Phylogenetics Evolutionary genetics References Delsuc, F., Brinkmann, H. & Philippe, H. Phylogenomics and the reconstruction of the tree of life. Nat. Rev. Genet. 6 , 361–375 (2005). Article CAS PubMed Google Scholar Telford, M.
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Article PubMed PubMed Central CAS Google Scholar Rannala, B. & Yang, Z. Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J. Mol. Evol. 43 , 304–311 (1996). This article introduces Bayesian methods to phylogenetics . Article CAS PubMed Google Scholar Li, S., Pearl, D. K. & Doss, H. Phylogenetic tree construction using Markov chain Monte Carlo. J. Am. Stat. Assoc. 95 , 493–508 (2000). Article Google Scholar Mau, B. & Newton, M. A. Phylogenetic Inference for binary data on dendograms using Markov chain Monte Carlo. J. Comput. Graph. Stat. 6 , 122–131 (1997). Google Scholar Huelsenbeck, J. P. & Ronquist, F.
Frequentist properties of Bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models. Syst. Biol. 53 , 904–913 (2004). Article PubMed Google Scholar Chen, M.-H., Kuo, L. & Lewis, P. (eds) Bayesian Phylogenetics: Methods, Algorithms, and Applications (Chapman and Hall/CRC, 2014). Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39 , 783 (1985). Article PubMed Google Scholar Susko, E. Bootstrap support is not first-order correct. Syst. Biol. 58 , 211–223 (2009). Article PubMed Google Scholar Yang, Z. & Zhu, T.
Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees. Proc. Natl Acad. Sci. USA 115 , 1854–1859 (2018). Article CAS PubMed PubMed Central Google Scholar Huelsenbeck, J. P. Performance of phylogenetic methods in simulation. Syst. Biol. 44 , 17–48 (1995). Article Google Scholar Baurain, D., Brinkmann, H. & Philippe, H. Lack of resolution in the animal phylogeny: closely spaced
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Lake, again using the conditioned reconstruction algorithm, proposes a ring-like model in which species of all three domains—Archaea, Bacteria, and Eukarya—evolved from a single pool of gene-swapping prokaryotes. His laboratory proposes that this structure is the best fit for data from extensive DNA analyses performed in his laboratory, and that the ring model is the only one that adequately takes HGT and genomic fusion into account. However, other phylogeneticists remain highly skeptical of this model. In summary, the “tree of life” model proposed by Darwin must be modified to include HGT. Does this mean abandoning the tree model completely? Even Lake argues that all attempts should be made to discover some modification of the tree model to allow it to accurately fit his data, and only the inability to do so will sway people toward his ring proposal. This doesn’t mean a tree, web, or a ring will correlate completely to an accurate description of phylogenetic relationships of life. A consequence of the new thinking about phylogenetic models is the idea that Darwin’s original conception of the phylogenetic tree is too simple, but made sense based on what was known at the time.
A major issue in all data collection for molecular phylogenetics is taxon sampling, which refers to the use of data from only a small representative set of species for inferring higher-level evolutionary history. Insufficient taxon sampling is often cited as a significant source of error in phylogenetic studies, and consequently, acquisition of large data sets is advocated. To test this assertion, we have conducted computer simulation studies by using natural collections of evolutionary parameters—rates of evolution, species sampling, and gene lengths—determined from data available in genomic databases. A comparison of the true tree with trees constructed by using taxa subsamples and trees constructed by using all taxa shows that the amount of phylogenetic error per internal branch is similar; a result that holds true for the neighbor-joining, minimum evolution, maximum parsimony, and maximum likelihood methods. Furthermore, our results show that even though trees inferred by using progressively larger taxa subsamples of a real data set become increasingly similar to trees inferred by using the full sample, all inferred trees are equidistant from the true tree in terms of phylogenetic error per internal branch. Our results suggest that longer sequences, rather than extensive sampling, will better improve the accuracy of phylogenetic inference.
Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10–100 times faster than assembly-based approaches and in most cases more accurate—the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale. Phylogenetic trees are generated from sequencing reads without genome assembly or annotation.
Abstract Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy.
In a benchmark encompassing a broad variety of datasets, Read2Tree is 10–100 times faster than assembly-based approaches and in most cases more accurate—the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.
Subject terms: Phylogeny, Genome informatics, Phylogenetics, Comparative genomics Phylogenetic trees are generated from sequencing reads without genome assembly or annotation. status released display-pdf yes is-in-collection-domain yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2022 Apr 18; Accepted 2023 Mar 16; Issue date 2024. Main Phylogenetic trees depict evolutionary relationships among biological entities. These entities can be species—as in the tree of life 1 – 4 .
They can also be cancerous cells in tumor progression trees 5 or developmental lineage trees 6 , viral and bacterial strains in infectious outbreaks 7 , cells, or genes in trees used to propagate molecular function annotations among model and nonmodel species 8 , 9 . Owing to this pervasiveness, methods to infer phylogenetic trees are among the most used and cited software tools in all of life sciences. In the context of species tree inference, the availability of genome-wide sequencing has made it routine to consider as many marker genes per taxon as the genomes provide.
Read2Tree is able to provide a full phylogenetic comparison of hundreds of samples in a fraction of time compared with current established pipelines. Crucially, the speedup is achieved without compromising the accuracy of the resulting trees. In addition, Read2Tree is able to also provide accurate trees and species comparisons using only low-coverage (0.1×) datasets as well as RNA versus genomic sequencing and operates on long or short reads. This makes Read2Tree a highly versatile method to obtain key insights from a single sample, scaling up to thousands of samples.
Faster and often more accurate than assembly-based trees Next, we compared the performances of Read2Tree with conventional assembly pipelines. For this, we generated de novo assemblies and protein predictions across the same datasets as from the previous section, using Canu 26 for PacBio and ONT data and Megahit 27 together with SoapDeNovo 28 for the Illumina reads ( Methods ). The conventional assemblies were processed using OMA standalone, including the same exported reference genomes, as OMA standalone was previously shown to identify the most accurate phylogenetic marker genes 29 .
While the alignment-free approach of Mash was much faster than even Read2Tree, the resulting trees were much less accurate than either Read2Tree or the assembly-based approach (Supplementary Fig. 5 ). This illustrates why alignment-free approaches such as Mash, while very useful for fast approximations, are typically not suitable to reconstruct high-quality phylogenetic trees. Accurate reconstruction of a 435 species yeast tree of life To assess a potential large-scale application for Read2Tree, we applied it to reconstruct a large yeast phylogeny from raw reads.
This will enable faster and more comprehensive phylogenetic reconstruction efforts—from tiny virus genomes to large eukaryotic ones, but also cell lineage, cancer trees and other kinds of phylogenies across biology and medicine. Methods Description of the Read2Tree method Read2Tree incorporates various publicly available tools for some of its steps (MAFFT 20 , NextGenMap 60 and Samtools 61 ) and uses these in a structured manner to go from reads and reference OGs to a concatenated alignment that is fed directly into a tree inference tool, which by default is IQTREE 24 .
Comprehensive phylogenetic trees are essential tools to better understand evolutionary processes. For many groups of organisms or projects aiming to build the Tree of Life, comprehensive phylogenetic analysis implies sampling hundreds to thousands of taxa. For the tree of all life this task rises to a highly conservative 13 million. Here, we assessed the performances of methods to reconstruct large trees using Monte Carlo simulations with parameters inferred from four large angiosperm DNA matrices, containing between 141 and 567 taxa. For each data set, parameters of the HKY85+G model were estimated and used to simulate 20 new matrices for sequence lengths from 100 to 10,000 base pairs. Maximum parsimony and neighbor joining were used to analyze each simulated matrix. In our simulations, accuracy was measured by counting the number of nodes in the model tree that were correctly inferred. The accuracy of the two methods increased very quickly with the addition of characters before reaching a plateau around 1000 nucleotides for any sizes of trees simulated. An increase in the number of taxa from 141 to 567 did not significantly decrease the accuracy of the methods used, despite the increase in the complexity of tree space. Moreover, the distribution of branch lengths rather than the rate of evolution was found to be the most important factor for accurately inferring these large trees. Finally, a tree containing 13,000 taxa was created to represent a hypothetical tree of all angiosperm genera and the efficiency of phylogenetic reconstructions was tested with simulated matrices containing an increasing number of nucleotides up to a maximum of 30,000. Even with such a large tree, our simulations suggested that simple heuristic searches were able to infer up to 80% of the nodes correctly.
Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the phylogenetic tree reconstruction based on the Maximum Likelihood method. In this study, we explored the utility of neural networks to predict the correct model of sequence evolution and the correct topology for four sequence alignments. We trained neural networks with different architectures using simulated nucleotide and amino acid sequence alignments for a wide range of evolutionary models, model parameters and branch lengths. By comparing the accuracy of model and topology prediction of the trained neural networks with Maximum Likelihood and Neighbour Joining methods, we show that for quartet trees, the neural network classifier outperforms the Neighbour Joining method and is in most cases as good as the Maximum Likelihood method to infer the best model of sequence evolution and the best tree topology. These results are consistent for nucleotide and amino acid sequence data. Furthermore, we found that neural network classifiers are much faster than the IQ-Tree implementation of the Maximum Likelihood method. Our results show that neural networks could become a true competitor for the Maximum Likelihood method in phylogenetic reconstructions.
Abstract
Inferring phylogenetic trees for a set of taxa is one of the primary objective in the evolutionary biology. Numerous approaches exist to reconstruct the phylogenetic trees by considering different biological data, such as DNA sequence, protein sequence, protein-protein interaction graph, etc. However, each method has its own strengths and weaknesses. Till date, no existing method guarantees to determine true phylogenetic trees all the times. Various studies identified distinct branch length configurations where the existing methods are inefficient to infer the correct tree topologies.
Here, we propose a novel deep convolutional neural network (CNN)-based model,
DeePhy
, to reconstruct the phylogenetic trees from the unaligned sequences. The sequences are repre- sented on a two-dimensional coordinate plane by utilizing a biological semantics-based map- ping. Additionally, to assess the robustness of a method, here we also propose a novel boot- strapping technique to generate replicas from the unaligned sequences. We train the model on the triplet sequences, where the output is a triplet tree topology.
We show that the well-trained DeePhy outperforms the state-of-the-art methods in inferring triplet tree topology. We experiment DeePhy on data simulated under numerous critical conditions and various branch length configurations. We conduct the McNemar test for comparing the performance of DeePhy and the state-of-the-art methods. The results exhibit that DeePhy is significantly more accurate and remarkably robust in determining the triplet tree topologies for most of the cases than that of the conventional methods. Again, various comparison metrics show that DeePhy also outperforms the conventional methods in inferring trees. Finally, to analyze the performance of DeePhy on real biological dataset, we apply it on Gadiformes dataset. Reassuringly, DeePhy reconstructs the phylogenetic tree from real biological data with known or widely accepted topologies.
Although various practical challenges still need to be taken care of, the outcomes of our study suggest that the deep learning approaches be a successful endeavour in inferring the accurate phylogenetic trees.
Abstract In the face of rapid biodiversity loss, many approaches have been developed to measure biodiversity in ways that go beyond species richness. One prominent example is Phylogenetic Diversity (PD), which measures evolutionary history by summing the branches required to connect a set of species on a dated phylogenetic tree. PD may also capture other biodiversity measures by proxy such as the richness of biological features and their potential future benefits for humanity, sometimes known as ‘future options’. The total global PD is known for some well-studied groups, such as most vertebrates, but PD estimates are lacking for the majority of the tree of life. Here, we characterize the distribution of PD across the complete tree of life with over 2.2 million species. To do this we use data from the Open Tree of Life and a smoothing method to interpolate between nodes without date information. We estimate that the PD represented by all described species together is between 29 and 33 trillion years. We characterize the distribution of evolutionary distinctiveness, a measure of the fair share of PD captured by individual species, across all life and within selected clades. Many clades have bimodal distributions of evolutionary distinctiveness across species which may be due to changes in diversification rate within subclades. PD has previously been used as the basis for conservation prioritization schemes such as EDGE (Evolutionary Distinct and Globally Endangered) which synthesizes phylogenetic tree data with extinction risk data from the IUCN Red List of threatened species. Here we estimate EDGE scores for over 130,000 species, many more than have been done previously. The top EDGE species is Latimeria chalumnae, the critically endangered West Indian Ocean coelacanth. We hope this work will pave the way for more complete and automated analyses of PD and EDGE scores across the complete tree of life.
Almost all standard phylogenetic methods for reconstructing gene trees result in unrooted trees; yet, many of the most useful applications of gene trees require that the gene trees be correctly rooted. As a result, several computational methods have been developed for inferring the root of unrooted gene trees. However, the accuracy of such methods has never been systematically evaluated on prokaryotic gene families, where horizontal gene transfer is often one of the dominant evolutionary events driving gene family evolution. In this work, we address this gap by conducting a thorough comparativ
PLoS One PLoS ONE 440 plosone 101285081 plos PLoS ONE 1932-6203 PLOS PMC7228096 PMC7228096.1 7228096 7228096 32413061 10.1371/journal.pone.0232950 PONE-D-19-30505 1 Research Article Biology and Life Sciences Evolutionary Biology Evolutionary Systematics Phylogenetics Phylogenetic Analysis Biology and Life Sciences Taxonomy Evolutionary Systematics Phylogenetics Phylogenetic Analysis Computer and Information Sciences Data Management Taxonomy Evolutionary Systematics Phylogenetics Phylogenetic Analysis Biology and Life Sciences Evolutionary Biology Evolutionary Genetics Research and Analysis Methods Simulation and Modeling Biology and Life Sciences Evolutionary Biology Evolutionary Processes Evolutionary Rate Biology and Life Sciences Evolutionary Biology Evolutionary Systematics Phylogenetics Biology and Life Sciences Taxonomy Evolutionary Systematics Phylogenetics Computer and Information Sciences Data Management Taxonomy Evolutionary Systematics Phylogenetics Biology and Life Sciences Organisms Bacteria Cyanobacteria Biology and Life Sciences Evolutionary Biology Evolutionary Processes Horizontal Gene Transfer Biology and Life Sciences Genetics Gene Transfer Horizontal Gene Transfer Research and Analysis Methods Database and Informatics Methods Assessing the accuracy of phylogenetic rooting methods on prokaryotic gene families Gene tree rooting Wade Taylor Data curation Formal analysis Investigation Software Validation Visualization Writing – original draft 1 Rangel L.
Almost all standard phylogenetic methods for reconstructing gene trees result in unrooted trees; yet, many of the most useful applications of gene trees require that the gene trees be correctly rooted. As a result, several computational methods have been developed for inferring the root of unrooted gene trees. However, the accuracy of such methods has never been systematically evaluated on prokaryotic gene families, where horizontal gene transfer is often one of the dominant evolutionary events driving gene family evolution.
Background Phylogenetic trees, or phylogenies, represent evolutionary relationships between biological entities. Gene trees , which represent the evolutionary relationships between distinct homologs of a gene family, and species trees , which represent evolutionary relationships between species (or other groupings of taxa), are the two most widely used kinds of phylogenetic
However, knowledge of how a phylogeny is rooted is fundamental to understanding how genes and species evolve and almost all applications of phylogenies require phylogenetic trees to be correctly rooted. As a result, several techniques have been developed for estimating the correct root position within a tree and these techniques are widely used [ 5 , 6 ]. Current rooting methods can be broadly classified into four categories. The first category includes methods that are designed specifically for species tree rooting. This category includes outgroup rooting [ 7 – 9 ], which is perhaps the most widely used method for species tree rooting.
Other methods designed for species tree rooting include phylogenomic methods based on phylogenetic reconciliation, where the species tree is rooted based on maximizing evolutionary “fit” with a collection of unrooted gene trees [ 10 – 12 ]. The second category consists of those methods that attempt to locate the root based on branch lengths on the inferred phylogeny.
This category includes strict molecular clock rooting [ 17 ], Bayesian molecular clock rooting [ 18 ], methods based on non-reversible and/or non-stationary evolutionary models [ 19 – 23 ], and relaxed clock models [ 24 , 25 ]. Finally, the fourth category of methods are those that are based on phylogenetic reconciliation. These methods are designed specifically for gene tree rooting and work by reconciling an unrooted gene tree with a known rooted species tree. This reconciliation is computed based on the duplication-loss model for eukaryotic gene families [ 26 ] and on the duplication-transfer-loss (DTL) reconciliation loss model for prokaryotic gene families [ 27 – 31 ].
It is worth noting that midpoint rooting, MAD rooting, and MV rooting critically use gene tree branch lengths to identify roots, while DTL rooting and ALE rooting ignore branch lengths and only use gene tree topologies (along with corresponding species tree topologies) for root identification. Not using branch lengths for gene tree rooting has both advantages and disadvantages. Gene tree branch lengths are directly impacted by substitution rate variation along tree edges, which can mislead methods that depend on branch-lengths for rooting. Branch lengths can also be difficult to infer accurately.
Reconciliation-based rooting methods and gene tree error Recall that results from the simulation study suggest that the rooting accuracy of reconciliation-based methods degrades rapidly with increasing phylogenetic inference error. The gene trees used in our empirical data analysis likely have high error rates, and this may partly explain why DTL rooting and ALE rooting performed poorly on the empirical data set. Several species-tree-aware gene tree reconstruction methods, such as ALE itself [ 27 ], TreeFix-DTL [ 34 ], and ecceTERA [ 29 ], have been developed over the past few years, and these methods have been shown to significantly reduce gene tree reconstruction error.
In phylogenetic analysis one study the relationship between different species. By comparing DNA from two different species it is possible to get a numerical value representing the difference between the species. For a set of species, all pair-wise comparisons result in a dissimilarity matrix d . In this thesis I present a few methods for constructing a phylogenetic tree from d . The common denominator for these methods is that they do not generate a tree, but instead give a connected graph. The resulting graph will be a tree, in areas where the data perfectly matches a tree. When d does not pe
historically was used to reconstruct phylogenetic trees, although direct comparison of genetic sequences is a more common method today. Evolutionary biologists
Evolution is the change in the heritable characteristics of biological populations over successive generations. It occurs when evolutionary processes such as genetic drift and natural selection act on genetic variation, resulting in certain characteristics becoming more or less common within a population over successive generations. The process of evolution has given rise to biodiversity at every
Evolution is the change in the heritable characteristics of biological populations over successive generations. It occurs when evolutionary processes such as genetic drift and natural selection act on genetic variation, resulting in certain characteristics becoming more or less common within a population over successive generations. The process of evolution has given rise to biodiversity at every level of biological organisation.
The scientific theory of evolution by natural selection was conceived independently by two British naturalists, Charles Darwin and Alfred Russel Wallace, in the mid-19th century as an explanation for why organisms are adapted to their physical and biological environments. The theory was first set out in detail in Darwin's book On the Origin of Species. Evolution by natural selection is established by observable facts about living organisms: (1) more offspring are often produced than can possibly survive; (2) traits vary among individuals with respect to their morphology, physiology, and behaviour; (3) different traits confer different rates of survival and reproduction (differential fitness); and (4) traits can be passed from generation to generation (heritability of fitness). In successive generations, members of a population are therefore more likely to be replaced by the offspring of parents with favourable characteristics for that environment.
In the early 20th century, competing ideas of evolution were refuted and evolution was combined with Mendelian inheritance and population genetics to give rise to modern evolutionary theory. In this synthesis the basis for heredity is in DNA molecules that pass information from generation to generation. The processes that change DNA in a population include natural selection, genetic drift, mutation, and gene flow.
All life on Earth—including humanity—shares a last universal common ancestor (LUCA), which lived approximately 3.5–3.8 billion years ago. The fossil record includes a progression from early biogenic graphite to microbial mat fossils to fossilised multicellular organisms. Existing patterns of biodiversity have been shaped by repeated formations of new species (speciation), changes…
This is a phylogenetic model where all three domains of life evolved from a pool of primitive prokaryotes. Lake, again using the conditioned reconstruction algorithm, proposes a ring-like model in which species of all three domains—Archaea, Bacteria, and Eukarya—evolved from a single pool of gene-swapping prokaryotes. His laboratory proposes that this structure is the best fit for data from extensive DNA analyses performed in his laboratory, and that the ring model is the only one that adequately takes HGT and genomic fusion into account. However, other phylogeneticists remain highly skeptical of this model. In summary, we must modify Darwin's “tree of life” model to include HGT. Does this mean abandoning the tree model completely? Even Lake argues that scientists should attempt to modify the tree model to allow it to accurately fit his data, and only the inability to do so will sway people toward his ring proposal. This doesn’t mean a tree, web, or a ring will correlate completely to an accurate description of phylogenetic relationships of life.
Everything we examined (13) — 11 independent sources
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