Reference Species Tree Research Articles

A nuclear phylogenomic tree of grasses (Poaceae) recovers current classification despite gene tree incongruence.

Grasses (Poaceae) comprise c. 11 800 species and are central to human livelihoods and terrestrial ecosystems. Knowing their relationships and evolutionary history is key to comparative research and crop breeding. Advances in genome-scale sequencing allow for increased breadth and depth of phylogenomic analyses, making it possible to infer a new reference species tree of the family. We inferred a comprehensive species tree of grasses by combining new and published sequences for 331 nuclear genes from genome, transcriptome, target enrichment and shotgun data. Our 1153-tip tree covers 79% of grass genera (including 21 genera sequenced for the first time) and all but two small tribes. We compared it to a newly inferred 910-tip plastome tree. We recovered most of the tribes and subfamilies previously established, despite pervasive incongruence among nuclear gene trees. The early diversification of the PACMAD clade could represent a hard polytomy. Gene tree-species tree reconciliation suggests that reticulation events occurred repeatedly. Nuclear-plastome incongruence is rare, with very few cases of supported conflict. We provide a robust framework for the grass tree of life to support research on grass evolution, including modes of reticulation, and genetic diversity for sustainable agriculture.

Completing gene trees without species trees in sub-quadratic time.

As genome-wide reconstruction of phylogenetic trees becomes more widespread, limitations of available data are being appreciated more than ever before. One issue is that phylogenomic datasets are riddled with missing data, and gene trees, in particular, almost always lack representatives from some species otherwise available in the dataset. Since many downstream applications of gene trees require or can benefit from access to complete gene trees, it will be beneficial to algorithmically complete gene trees. Also, gene trees are often unrooted, and rooting them is useful for downstream applications. While completing and rooting a gene tree with respect to a given species tree has been studied, those problems are not studied in depth when we lack such a reference species tree. We study completion of gene trees without a need for a reference species tree. We formulate an optimization problem to complete the gene trees while minimizing their quartet distance to the given set of gene trees. We extend a seminal algorithm by Brodal et al. to solve this problem in quasi-linear time. In simulated studies and on a large empirical data, we show that completion of gene trees using other gene trees is relatively accurate and, unlike the case where a species tree is available, is unbiased. Our method, tripVote, is available at https://github.com/uym2/tripVote. Supplementary data are available at Bioinformatics online.

Seaview Version 5: A Multiplatform Software for Multiple Sequence Alignment, Molecular Phylogenetic Analyses, and Tree Reconciliation.

We present Seaview version 5, a multiplatform program to perform multiple alignment and phylogenetic tree building from molecular sequence data. Seaview provides network access to sequence databases, alignment with arbitrary algorithm, parsimony, distance and maximum likelihood tree building with PhyML, and display, printing, and copy-to-clipboard or to SVG files of rooted or unrooted, binary or multifurcating phylogenetic trees. While Seaview is primarily a program providing a graphical user interface to guide the user into performing desired analyses, Seaview possesses also a command-line mode adequate for user-provided scripts. Seaview version 5 introduces the ability to reconcile a gene tree with a reference species tree and use this reconciliation to root and rearrange the gene tree. Seaview is freely available at http://doua.prabi.fr/software/seaview .

Sequencing and Analyzing the Transcriptomes of a Thousand Species Across the Tree of Life for Green Plants.

The 1,000 Plants (1KP) initiative was the first large-scale effort to collect next-generation sequencing (NGS) data across a phylogenetically representative sampling of species for a major clade of life, in this case theViridiplantae, or green plants. As an international multidisciplinary consortium, we focused on plant evolution and its practical implications. Among the major outcomes were the inference of a reference species tree for green plants by phylotranscriptomic analysis of low-copy genes, a survey of paleopolyploidy (whole-genome duplications) across the Viridiplantae, the inferred evolutionary histories for many gene families and biological processes, the discovery of novel light-sensitive proteins for optogenetic studies in mammalian neuroscience, and elucidation of the genetic network for a complex trait (C4 photosynthesis). Altogether, 1KP demonstrated how value can be extracted from a phylodiverse sequencing data set, providing a template for future projects that aim to generate even more data, including complete de novo genomes, across the tree of life.

Non-parametric correction of estimated gene trees using TRACTION

MotivationEstimated gene trees are often inaccurate, due to insufficient phylogenetic signal in the single gene alignment, among other causes. Gene tree correction aims to improve the accuracy of an estimated gene tree by using computational techniques along with auxiliary information, such as a reference species tree or sequencing data. However, gene trees and species trees can differ as a result of gene duplication and loss (GDL), incomplete lineage sorting (ILS), and other biological processes. Thus gene tree correction methods need to take estimation error as well as gene tree heterogeneity into account. Many prior gene tree correction methods have been developed for the case where GDL is present.ResultsHere, we study the problem of gene tree correction where gene tree heterogeneity is instead due to ILS and/or HGT. We introduce TRACTION, a simple polynomial time method that provably finds an optimal solution to the RF-optimal tree refinement and completion (RF-OTRC) Problem, which seeks a refinement and completion of a singly-labeled gene tree with respect to a given singly-labeled species tree so as to minimize the Robinson−Foulds (RF) distance. Our extensive simulation study on 68,000 estimated gene trees shows that TRACTION matches or improves on the accuracy of well-established methods from the GDL literature when HGT and ILS are both present, and ties for best under the ILS-only conditions. Furthermore, TRACTION ties for fastest on these datasets. We also show that a naive generalization of the RF-OTRC problem to multi-labeled trees is possible, but can produce misleading results where gene tree heterogeneity is due to GDL.

Inference of Ancient Whole-Genome Duplications and the Evolution of Gene Duplication and Loss Rates.

Gene tree-species tree reconciliation methods have been employed for studying ancient whole-genome duplication (WGD) events across the eukaryotic tree of life. Most approaches have relied on using maximum likelihood trees and the maximum parsimony reconciliation thereof to count duplication events on specific branches of interest in a reference species tree. Such approaches do not account for uncertainty in the gene tree and reconciliation, or do so only heuristically. The effects of these simplifications on the inference of ancient WGDs are unclear. In particular, the effects of variation in gene duplication and loss rates across the species tree have not been considered. Here, we developed a full probabilistic approach for phylogenomic reconciliation-based WGD inference, accounting for both gene tree and reconciliation uncertainty using a method based on the principle of amalgamated likelihood estimation. The model and methods are implemented in a maximum likelihood and Bayesian setting and account for variation of duplication and loss rates across the species tree, using methods inspired by phylogenetic divergence time estimation. We applied our newly developed framework to ancient WGDs in land plants and investigated the effects of duplication and loss rate variation on reconciliation and gene count based assessment of these earlier proposed WGDs.

Taxogenomics of the order Chlamydiales.

Bacterial classification is a long-standing problem for taxonomists and species definition itself is constantly debated among specialists. The classification of strict intracellular bacteria such as members of the order Chlamydiales mainly relies on DNA- or protein-based phylogenetic reconstructions because these organisms exhibit few phenotypic differences and are difficult to culture. The availability of full genome sequences allows the comparison of the performance of conserved protein sequences to reconstruct Chlamydiales phylogeny. This approach permits the identification of markers that maximize the phylogenetic signal and the robustness of the inferred tree. In this study, a set of 424 core proteins was identified and concatenated to reconstruct a reference species tree. Although individual protein trees present variable topologies, we detected only few cases of incongruence with the reference species tree, which were due to horizontal gene transfers. Detailed analysis of the phylogenetic information of individual protein sequences (i) showed that phylogenies based on single randomly chosen core proteins are not reliable and (ii) led to the identification of twenty taxonomically highly reliable proteins, allowing the reconstruction of a robust tree close to the reference species tree. We recommend using these protein sequences to precisely classify newly discovered isolates at the family, genus and species levels.

Robust Markers Reflecting Phylogeny and Taxonomy of Rhizobia

Genomic ANI (Average Nucleotide Identity) has been found to be able to replace DNA-DNA hybridization in prokaryote taxonomy. The ANI of each of the core genes that has a phylogeny congruent with the reference species tree of rhizobia was compared to the genomic ANI. This allowed us to identify three housekeeping genes (SMc00019-truA-thrA) whose ANI reflected the intraspecies and interspecies genomic ANI among rhizobial strains, revealing an ANI gap (≥2%) between the inter- and intra-species comparisons. The intraspecies (96%) and interspecies (94%) ANI boundaries calculated from three genes (SMc00019-truA-thrA) provided a criterion for bacterial species definition and confirmed 621/629 of known interspecies relationships within Bradyrhizobium, Mesorhizobium, Sinorhizobium and Rhizobium. Some widely studied strains should be renamed. The SMc00019-truA-thrA ANI also correlates well with the genomic ANI of strains in Agrobacterium, Methylobacterium, Ralstonia, Rhodopseudomonas, Cupriavidus and Burkholderia, suggesting their wide applicability in other bacteria.

FUNYBASE: a FUNgal phYlogenomic dataBASE

BackgroundThe increasing availability of fungal genome sequences provides large numbers of proteins for evolutionary and phylogenetic analyses. However the heterogeneity of data, including the quality of genome annotation and the difficulty of retrieving true orthologs, makes such investigations challenging. The aim of this study was to provide a reliable and integrated resource of orthologous gene families to perform comparative and phylogenetic analyses in fungi.DescriptionFUNYBASE is a database dedicated to the analysis of fungal single-copy genes extracted from available fungal genomes sequences, their classification into reliable clusters of orthologs, and the assessment of their informative value for phylogenetic reconstruction based on amino acid sequences. The current release of FUNYBASE contains two types of protein data: (i) a complete set of protein sequences extracted from 30 public fungal genomes and classified into clusters of orthologs using a robust automated procedure, and (ii) a subset of 246 reliable ortholog clusters present as single copy genes in 21 fungal genomes. For each of these 246 ortholog clusters, phylogenetic trees were reconstructed based on their amino acid sequences. To assess the informative value of each ortholog cluster, each was compared to a reference species tree constructed using a concatenation of roughly half of the 246 sequences that are best approximated by the WAG evolutionary model. The orthologs were classified according to a topological score, which measures their ability to recover the same topology as the reference species tree. The full results of these analyses are available on-line with a user-friendly interface that allows for searches to be performed by species name, the ortholog cluster, various keywords, or using the BLAST algorithm. Examples of fruitful utilization of FUNYBASE for investigation of fungal phylogenetics are also presented.ConclusionFUNYBASE constitutes a novel and useful resource for two types of analyses: (i) comparative studies can be greatly facilitated by reliable clusters of orthologs across sets of user-defined fungal genomes, and (ii) phylogenetic reconstruction can be improved by identifying genes with the highest informative value at the desired taxonomic level.

Identifying Coevolving Partners from Paralogous Gene Families

Many methods have been developed to detect coevolution from aligned sequences. However, all the existing methods require a one-to-one mapping of candidate coevolving partners (nucleotides, amino acids) a priori. When two families of sequences have distinct duplication and loss histories, finding the one-to-one mapping of coevolving partners can be computationally involved. We propose an algorithm to identify the coevolving partners from two families of sequences with distinct phylogenetic trees. The algorithm maps each gene tree to a reference species tree, and builds a joint state of sequence composition and assignments of coevolving partners for each species tree node. By applying dynamic programming on the joint states, the optimal assignments can be identified. Time complexity is quadratic to the size of the species tree, and space complexity is exponential to the maximum number of gene tree nodes mapped to the same species tree node. Analysis on both simulated data and Pfam protein domain sequences demonstrates that the paralog coevolution algorithm picks up the coevolving partners with 60% 88% accuracy. This algorithm extends phylogeny-based coevolutionary models and make them applicable to a wide range of problems such as predicting protein-protein, protein-DNA and DNA-RNA interactions of two distinct families of sequences.